Image processing project list for java and dotnet

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Image processing for java / dot net
1.Captcha as Graphical Passwords—A New Security
Primitive Based on Hard AI Problems.
Synopsis:
Many security primitives are based on hard mathematical problems. Using hard AI problems
for security is emerging as an exciting new paradigm, but has been under-explored. In this
paper, we present a new security primitive based on hard AI problems, namely, a novel
family of graphical password systems built on top of Captcha technology, which we call
Captcha as graphical passwords (CaRP). CaRP is both a Captcha and a graphical
password scheme. CaRP addresses a number of security problems altogether, such as
online guessing attacks, relay attacks, and, if combined with dual-view technologies,
shoulder-surfing attacks. Notably, a CaRP password can be found only probabilistically by
automatic online guessing attacks even if the password is in the search set. CaRP also
offers a novel approach to address the well-known image hotspot problem in popular
graphical password systems, such as PassPoints, that often leads to weak password
choices. CaRP is not a panacea, but it offers reasonable security and usability and appears
to fit well with some practical applications for improving online security.
2.An Adaptive Cloud Downloading Service.
Synopsis:
Video content downloading using the P2P approach is scalable, but does not always give
good performance. Recently, subscription-based premium services have emerged, referred
to as cloud downloading. In this service, the cloud storage and server caches user-
interested content and updates the cache based on user downloading requests. If a
requested video is not in the cache, the request is held in a waiting state until the cache is
updated. We call this design server mode. An alternative design is to let the cloud server
serve all downloading requests as soon as they arrive, behaving as a helper peer. We call
this design helper mode. Our model and analysis show that both these designs are useful
for certain operating regimes. The helper mode is good at handling a high request rate,
while the server mode is good at scaling with video population size. We design an adaptive
algorithm (AMS) to select the service mode automatically. Intuitively, AMS switches service
mode from server mode to helper mode when too many peers request blocked movies, and
vice versa. The ability of AMS to achieve good performance in different operating regimes is
validated by simulation .

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3. Learn to Personalized Image Search from the Photo
Sharing Websites.
Synopsis:
Increasingly developed social sharing websites like Flickr and Youtube allow users
to create, share, annotate, and comment medias. The large-scale user-generated
metadata not only facilitate users in sharing and organizing multimedia content, but
provide useful information to improve media retrieval and management.
Personalized search serves as one of such examples where the web search
experience is improved by generating the returned list according to the modified user
search intents. In this paper, we exploit the social annotations and propose a novel
framework simultaneously considering the user and query relevance to learn to
personalized image search. The basic premise is to embed the user preference and
query-related search intent into user-specific topic spaces. Since the users' original
annotation is too sparse for topic modeling, we need to enrich users' annotation pool
before user-specific topic spaces construction. The proposed framework contains
two components: (1) a ranking-based multicorrelation tensor factorization model is
proposed to perform annotation prediction, which is considered as users' potential
annotations for the images; (2) we introduce user-specific topic modeling to map the
query relevance and user preference into the same user-specific topic space. For
performance evaluation, two resources involved with users' social activities are
employed. Experiments on a large-scale Flickr dataset demonstrate the
effectiveness of the proposed method.
4.Learning and Recognition of On-Premise Signs from
Weakly Labeled Street View Images.
Synopsis:
Camera-enabled mobile devices are commonly used as interaction platforms for linking the
user's virtual and physical worlds in numerous research and commercial applications, such
as serving an augmented reality interface for mobile information retrieval. The various
application scenarios give rise to a key technique of daily life visual object recognition. On-
premise signs (OPSs), a popular form of commercial advertising, are widely used in our
living life. The OPSs often exhibit great visual diversity (e.g., appearing in arbitrary size),
accompanied with complex environmental conditions (e.g., foreground and background
clutter). Observing that such real-world characteristics are lacking in most of the existing
image data sets, in this paper, we first proposed an OPS data set, namely OPS-62, in which
totally 4649 OPS images of 62 different businesses are collected from Google's Street

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View. Further, for addressing the problem of real-world OPS learning and recognition, we
developed a probabilistic framework based on the distributional clustering, in which we
proposed to exploit the distributional information of each visual feature (the distribution of its
associated OPS labels) as a reliable selection criterion for building discriminative OPS
models. Experiments on the OPS-62 data set demonstrated the outperformance of our
approach over the state-of-the-art probabilistic latent semantic analysis models for more
accurate recognitions and less false alarms, with a significant 151.28% relative
improvement in the average recognition rate. Meanwhile, our approach is simple, linear,
and can be executed in a parallel fashion, making it practical and scalable for large-scale
multimedia applications..
5.An extended visual cryptography scheme without pixel
expansion for halftone images.
Synopsis:
Visual cryptography is a secret sharing scheme which uses images distributed as shares
such that, when the shares are superimposed, a hidden secret image is revealed. In
extended visual cryptography, the share images are constructed to contain meaningful
cover images, thereby providing opportunities for integrating visual cryptography and
biometric security techniques. In this paper, we propose a method for processing halftone
images that improves the quality of the share images and the recovered secret image in an
extended visual cryptography scheme for which the size of the share images and the
recovered image is the same as for the original halftone secret image. The resulting
scheme maintains the perfect security of the original extended visual cryptography
approach.
6.Trust modeling in social tagging of multimedia content.
Synopsis:
Tagging in online social networks is very popular these days, as it facilitates search and
retrieval of multimedia content. However, noisy and spam annotations often make it difficult
to perform an efficient search. Users may make mistakes in tagging and irrelevant tags and
content may be maliciously added for advertisement or self-promotion. This article surveys
recent advances in techniques for combatting such noise and spam in social tagging. We
classify the state-of-the-art approaches into a few categories and study representative
examples in each. We also qualitatively compare and contrast them and outline open issues
for future research.

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7.Personalized Geo-Speciﬁc Tag Recommendation for
Photos on Social Websites.
Synopsis:
Social tagging becomes increasingly important to organize and search large-scale
community-contributed photos on social websites. To facilitate generating high-quality social
tags, tag recommendation by automatically assigning relevant tags to photos draws
particular research interest. In this paper, we focus on the personalized tag
recommendation task and try to identify user-preferred, geo-location-specific as well as
semantically relevant tags for a photo by leveraging rich contexts of the freely available
community-contributed photos. For users and geo-locations, we assume they have different
preferred tags assigned to a photo, and propose a subspace learning method to individually
uncover the both types of preferences. The goal of our work is to learn a unified subspace
shared by the visual and textual domains to make visual features and textual information of
photos comparable. Considering the visual feature is a lower level representation on
semantics than the textual information, we adopt a progressive learning strategy by
additionally introducing an intermediate subspace for the visual domain, and expect it to
have consistent local structure with the textual space. Accordingly, the unified subspace is
mapped from the intermediate subspace and the textual space respectively. We formulate
the above learning problems into a united form, and present an iterative optimization with its
convergence proof. Given an untagged photo with its geo-location to a user, the user-
preferred and the geo-location-specific tags are found by the nearest neighbor search in the
corresponding unified spaces. Then we combine the obtained tags and the visual
appearance of the photo to discover the semantically and visually related photos, among
which the most frequent tags are used as the recommended tags. Experiments on a large-
scale data set collected from Flickr verify the effectivity of the proposed solution.
8. Beyond Text QA: Multimedia Answer Generation by
Harvesting Web Information.
Synopsis:
Community question answering (cQA) services have gained popularity over the past
years. It not only allows community members to post and answer questions but also
enables general users to seek information from a comprehensive set of well-answered
questions. However, existing cQA forums usually provide only textual answers, which are
not informative enough for many questions. In this paper, we propose a scheme that is able
to enrich textual answers in cQA with appropriate media data. Our scheme consists of three
components: answer medium selection, query generation for multimedia search, and
multimedia data selection and presentation. This approach automatically determines which

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type of media information should be added for a textual answer. It then automatically
collects data from the web to enrich the answer. By processing a large set of QA pairs and
adding them to a pool, our approach can enable a novel multimedia question answering
(MMQA) approach as users can find multimedia answers by matching their questions with
those in the pool. Different from a lot of MMQA research efforts that attempt to directly
answer questions with image and video data, our approach is built based on community-
contributed textual answers and thus it is able to deal with more complex questions. We
have conducted extensive experiments on a multi-source QA dataset. The results
demonstrate the effectiveness of our approach.
9. Bootstrapping Visual Categorization With Relevant
Negatives.
Learning classifiers for many visual concepts are important for image categorization and
retrieval. As a classifier tends to misclassify negative examples which are visually similar to
positive ones, inclusion of such misclassified and thus relevant negatives should be
stressed during learning. User-tagged images are abundant online, but which images are
the relevant negatives remains unclear. Sampling negatives at random is the de facto
standard in the literature. In this paper, we go beyond random sampling by proposing
Negative Bootstrap. Given a visual concept and a few positive examples, the new algorithm
iteratively finds relevant negatives. Per iteration, we learn from a small proportion of many
user-tagged images, yielding an ensemble of meta classifiers. For efficient classification, we
introduce Model Compression such that the classification time is independent of the
ensemble size. Compared with the state of the art, we obtain relative gains of 14% and 18%
on two present-day benchmarks in terms of mean average precision. For concept search in
one million images, model compression reduces the search time from over 20 h to
approximately 6 min. The effectiveness and efficiency, without the need of manually
labeling any negatives, make negative bootstrap appealing for learning better visual
concept classifiers.
10.Circular Reranking for Visual Search.
Synopsis:
Search reranking is regarded as a common way to boost retrieval precision. The problem
nevertheless is not trivial especially when there are multiple features or modalities to be
considered for search, which often happens in image and video retrieval. This paper
proposes a new reranking algorithm, named circular reranking, that reinforces the mutual
exchange of information across multiple modalities for improving search performance,
following the philosophy that strong performing modality could learn from weaker ones,
while weak modality does benefit from interacting with stronger ones. Technically, circular
reranking conducts multiple runs of random walks through exchanging the ranking scores

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among different features in a cyclic manner. Unlike the existing techniques, the reranking
procedure encourages interaction among modalities to seek a consensus that are useful for
reranking. In this paper, we study several properties of circular reranking, including how and
which order of information propagation should be configured to fully exploit the potential of
modalities for reranking. Encouraging results are reported for both image and video retrieval
on Microsoft Research Asia Multimedia image dataset and TREC Video Retrieval
Evaluation 2007-2008 datasets, respectively.
11 Hierarchical Super-Resolution-Based Inpainting.
Synopsis:
This paper introduces a novel framework for examplar-based inpainting. It consists in
performing first the inpainting on a coarse version of the input image. A hierarchical super-
resolution algorithm is then used to recover details on the missing areas. The advantage of
this approach is that it is easier to inpaint low-resolution pictures than high-resolution ones.
The gain is both in terms of computational complexity and visual quality. However, to be
less sensitive to the parameter setting of the inpainting method, the low-resolution input
picture is inpainted several times with different configurations. Results are efficiently
combined with a loopy belief propagation and details are recovered by a single-image
super-resolution algorithm. Experimental results in a context of image editing and texture
synthesis demonstrate the effectiveness of the proposed method. Results are compared to
five state-of-the-art inpainting methods.
12.Image Authentication Using Stochastic Diffusion.
Synopsis:
This paper considers an approach to encrypted information hiding based on Stochastic
Diffusion for encrypting digital images coupled with the application of a Least Significant Bit
(LSB) method for information embedding. After providing a brief summary of various
information hiding methods based on spatial and transform domain techniques, two new
methods are introduced. The first of these considers a binary image watermarking algorithm
for hiding an image in a single host image which is based on binarization of the encrypted
data. The second method extends this approach to solving the problem of 24-bit image
hiding in three host images which generates a near perfect reconstruction after decryption.
Both methods make use of a 'hidden code' technique to randomize the order of the
embedded bits and the location (in the image plane) of the LSBs which make the embedded
information more robust to attack. Details of the algorithms developed are provided and
examples are given, which have application in the field of covert cryptography and the
authentication of full colour images for copyright protection and Data Rights Management.

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13.Scalable Face Image Retrieval using Attribute-Enhanced
Sparse Codewords.
Synopsis:
Photos with people (e.g., family, friends, celebrities, etc.) are the major interest of users.
Thus, with the exponentially growing photos, large-scale content-based face image retrieval
is an enabling technology for many emerging applications. In this work, we aim to utilize
automatically detected human attributes that contain semantic cues of the face photos to
improve content-based face retrieval by constructing semantic codewords for efficient large-
scale face retrieval. By leveraging human attributes in a scalable and systematic framework,
we propose two orthogonal methods named attribute-enhanced sparse coding and
attribute-embedded inverted indexing to improve the face retrieval in the offline and online
stages. We investigate the effectiveness of different attributes and vital factors essential for
face retrieval. Experimenting on two public datasets, the results show that the proposed
methods can achieve up to 43.5% relative improvement in MAP compared to the existing
methods..
14. Understanding the External Links of Video.
Synopsis:
Recently, many video sharing sites provide external links so that their video or audio
contents can be embedded into external web sites. For example, users can copy the
embedded URLs of the videos of YouTube and post the URL links on their own blogs.
Clearly, the purpose of such function is to increase the distribution of the videos and the
associated advertisement. Does this function fulfill its purpose and what is the
quantification? In this paper, we provide a comprehensive measurement study and analysis
on these external links to answer these two questions. With the traces collected from two
major video sharing sites, YouTube and Youku of China, we show that the external links
have various impacts on the popularity of the video sharing sites. More specifically, for
videos that have been uploaded for eight months in Youku, around 15% of views can come
from external links. Some contents are densely linked. For example, comedy videos can
attract more than 800 external links on average. We also study the relationship between the
external links and the internal links. We show that there are correlations; for example, if a
video is popular itself, it is likely to have a large number of external links. Another
observation we find is that the external links usually have a higher impact on Youku than
that of YouTube. We conjecture that it is more likely that the external links have higher
impact for a regional site than a worldwide site.

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15. A Compressive Sensing based Secure Watermark
Detection and Privacy Preserving Storage.
Synopsis:
Privacy is a critical issue when the data owners outsource data storage or processing to a
third party computing service, such as the cloud. In this paper, we identify a cloud
computing application scenario that requires simultaneously performing secure watermark
detection and privacy preserving multimedia data storage. We then propose a compressive
sensing (CS)-based framework using secure multiparty computation (MPC) protocols to
address such a requirement. In our framework, the multimedia data and secret watermark
pattern are presented to the cloud for secure watermark detection in a CS domain to protect
the privacy. During CS transformation, the privacy of the CS matrix and the watermark
pattern is protected by the MPC protocols under the semi-honest security model. We derive
the expected watermark detection performance in the CS domain, given the target image,
watermark pattern, and the size of the CS matrix (but without the CS matrix itself). The
correctness of the derived performance has been validated by our experiments. Our
theoretical analysis and experimental results show that secure watermark detection in the
CS domain is feasible. Our framework can also be extended to other collaborative secure
signal processing and data-mining applications in the cloud.
16.LLSURE: Local Linear SURE-Based Edge-Preserving
Image Filtering.
Synopsis:
In this paper, we propose a novel approach for performing high-quality edge-preserving
image filtering. Based on a local linear model and using the principle of Stein's unbiased
risk estimate as an estimator for the mean squared error from the noisy image only, we
derive a simple explicit image filter which can filter out noise while preserving edges and
fine-scale details. Moreover, this filter has a fast and exact linear-time algorithm whose
computational complexity is independent of the filtering kernel size; thus, it can be applied
to real time image processing tasks. The experimental results demonstrate the
effectiveness of the new filter for various computer vision applications, including noise
reduction, detail smoothing and enhancement, high dynamic range compression, and
flash/no-flash denoising.
17. Altered Fingerprints: Analysis and Detection.
Synopsis:

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The widespread deployment of Automated Fingerprint Identification Systems (AFIS) in law
enforcement and border control applications has heightened the need for ensuring that
these systems are not compromised. While several issues related to fingerprint system
security have been investigated, including the use of fake fingerprints for masquerading
identity, the problem of fingerprint alteration or obfuscation has received very little attention.
Fingerprint obfuscation refers to the deliberate alteration of the fingerprint pattern by an
individual for the purpose of masking his identity. Several cases of fingerprint obfuscation
have been reported in the press. Fingerprint image quality assessment software (e.g.,
NFIQ) cannot always detect altered fingerprints since the implicit image quality due to
alteration may not change significantly. The main contributions of this paper are: 1)
compiling case studies of incidents where individuals were found to have altered their
fingerprints for circumventing AFIS, 2) investigating the impact of fingerprint alteration on
the accuracy of a commercial fingerprint matcher, 3) classifying the alterations into three
major categories and suggesting possible countermeasures, 4) developing a technique to
automatically detect altered fingerprints based on analyzing orientation field and minutiae
distribution, and 5) evaluating the proposed technique and the NFIQ algorithm on a large
database of altered fingerprints provided by a law enforcement agency. Experimental
results show the feasibility of the proposed approach in detecting altered fingerprints and
highlight the need to further pursue this problem.
18.A Novel Video Steganography based on Non-uniform
Rectangular Partition.
Synopsis:
This paper proposes a novel Video Steganography which can hide an uncompressed secret
video stream in a host video stream with almost the same size. Each frame of the secret
video will be Non-uniform rectangular partitioned and the partitioned codes obtained can be
an encrypted version of the original frame. These codes will be hidden in the Least 4
Significant Bits of each frames of the host video. Experimental results showed that this
algorithm can hide a same-size video in the host video without obvious distortion in the host
video.
19. A New Iterative Triclass Thresholding Technique in
Image Segmentation.
Synopsis:
We present a new method in image segmentation that is based on Otsu's method but
iteratively searches for subregions of the image for segmentation, instead of treating the full
image as a whole region for processing. The iterative method starts with Otsu's threshold

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and computes the mean values of the two classes as separated by the threshold. Based on
the Otsu's threshold and the two mean values, the method separates the image into three
classes instead of two as the standard Otsu's method does. The first two classes are
determined as the foreground and background and they will not be processed further. The
third class is denoted as a to-be-determined (TBD) region that is processed at next iteration.
At the succeeding iteration, Otsu's method is applied on the TBD region to calculate a new
threshold and two class means and the TBD region is again separated into three classes,
namely, foreground, background, and a new TBD region, which by definition is smaller than
the previous TBD regions. Then, the new TBD region is processed in the similar manner.
The process stops when the Otsu's thresholds calculated between two iterations is less
than a preset threshold. Then, all the intermediate foreground and background regions are,
respectively, combined to create the final segmentation result. Tests on synthetic and real
images showed that the new iterative method can achieve better performance than the
standard Otsu's method in many challenging cases, such as identifying weak objects and
revealing fine structures of complex objects while the added computational cost is minimal.
20. Local Directional Number Pattern for Face Analysis:
Face and Expression Recognition.
Synopsis:
This paper proposes a novel local feature descriptor, local directional number pattern
(LDN), for face analysis, i.e., face and expression recognition. LDN encodes the directional
information of the face's textures (i.e., the texture's structure) in a compact way, producing a
more discriminative code than current methods. We compute the structure of each micro-
pattern with the aid of a compass mask that extracts directional information, and we encode
such information using the prominent direction indices (directional numbers) and sign-which
allows us to distinguish among similar structural patterns that have different intensity
transitions. We divide the face into several regions, and extract the distribution of the LDN
features from them. Then, we concatenate these features into a feature vector, and we use
it as a face descriptor. We perform several experiments in which our descriptor performs
consistently under illumination, noise, expression, and time lapse variations. Moreover, we
test our descriptor with different masks to analyze its performance in different face analysis
tasks.
21. AnnoSearch: Image Auto-Annotation by Search.
Synopsis:
Although it has been studied for several years by computer vision and machine learning
communities, image annotation is still far from practical. In this paper, we present

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AnnoSearch, a novel way to annotate images using search and data mining technologies.
Leveraging the Web-scale images, we solve this problem in two-steps: 1) searching for
semantically and visually similar images on the Web, 2) and mining annotations from them.
Firstly, at least one accurate keyword is required to enable text-based search for a set of
semantically similar images. Then content-based search is performed on this set to retrieve
visually similar images. At last, annotations are mined from the descriptions (titles, URLs
and surrounding texts) of these images. It worth highlighting that to ensure the efficiency,
high dimensional visual features are mapped to hash codes which significantly speed up the
content-based search process. Our proposed approach enables annotating with unlimited
vocabulary, which is impossible for all existing approaches. Experimental results on real
web images show the effectiveness and efficiency of the proposed algorithm.
22. On the Typical Statistic Features for Image Blind
Steganalysis.
Synopsis:
Multimedia content is a suitable carrier for secret communication. This paper
focuses on the steganalysis technique which aims to get the forensic of secrecy existing in
multimedia carriers. A key concern for designing a blind steganalysis algorithm is the
selection of statistic features. The Probability Density Function (PDF) moment and
Characteristic Function (CF) moment are two typical kinds of statistic features commonly
used in blind steganalysis. And generally, the features are computed from the subbands of
transform domains, such as the wavelet coefficient subbands, the prediction subbands of
wavelet coefficients, the prediction error subbands of wavelet coefficients, the wavelet
coefficient subbands of image noise, and the log prediction error subbands of wavelet
coefficients. To decide which feature is more sensitive to message embedding and useful
for steganalysis is important and urgent. Till now, few works have focused on this topic, and
they can only give some experimental results without theoretical analysis. Additionally, few
frequency subbands have been investigated. To solve this problem, this paper reviews
existing feature computing algorithms, compares the two kinds of features, the PDF
moments and the CF moments, by analyzing the change trends of the statistic distribution
parameters of various frequency subbands before and after message embedding, and so
that provides a theoretical basis for the steganalysis feature selection and extraction. These
theoretical results are further confirmed by experimental results. This is the first work to
provide thorough theoretical analysis on so many feature computing algorithms. It is
expected to provide valuable information to researchers or engineers working in the field of
steganography forensics or steganalysis.
23. An Iranian License Plate Recognition System Based on
Color Features.

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Synopsis:
In this paper, an Iranian vehicle license plate recognition system based on a new
localization approach, which is modified to reflect the local context, is proposed, along with
a hybrid classifier that recognizes license plate characters. The method presented here is
based on a modified template-matching technique by the analysis of target color pixels to
detect the location of a vehicle's license plate. A modified strip search enables localization
of the standard color-geometric template utilized in Iran and several European countries.
This approach uses periodic strip search to find the hue of each pixel on demand. In
addition, when a group of target pixels is detected, it is analyzed to verify that its shape and
aspect ratio match those of the standard license plate. In addition to being scale and
rotation invariant, this method avoids time-consuming image algorithms and transformations
for the whole image pixels, such as resizing and Hough, Fourier, and wavelet transforms,
thereby cutting down the detection response time. License plate characters are recognized
by a hybrid classifier that comprises a decision tree and a support vector machine with a
homogeneous fifth-degree polynomial kernel. The performance detection rate and the
overall system performance achieved are 96% and 94%, respectively.
24. Perceptual Video Coding Based on SSIM-Inspired.
Synopsis:
We propose a perceptual video coding framework based on the divisive normalization
scheme, which is found to be an effective approach to model the perceptual sensitivity of
biological vision, but has not been fully exploited in the context of video coding. At the
macroblock (MB) level, we derive the normalization factors based on the structural similarity
(SSIM) index as an attempt to transform the discrete cosine transform domain frame
residuals to a perceptually uniform space. We further develop an MB level perceptual mode
selection scheme and a frame level global quantization matrix optimization method.
Extensive simulations and subjective tests verify that, compared with the H.264/AVC video
coding standard, the proposed method can achieve significant gain in terms of rate-SSIM
performance and provide better visual quality.
25. Compressed-Sensing-Enabled Video Streaming for
Wireless Multimedia Sensor Networks.
Synopsis:

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This paper presents the design of a networked system for joint compression, rate control and error correction of video
over resource-constrained embedded devices based on the theory of Compressed Sensing (CS). The objective of
this work is to design a cross-layer system that jointly controls the video encoding rate, the transmission rate, and the
channel coding rate to maximize the received video quality. First, compressed sensing-based video encoding for
transmission over Wireless Multimedia Sensor Networks (WMSNs) is studied. It is shown that compressed sensing
can overcome many of the current problems of video over WMSNs, primarily encoder complexity and low resiliency
to channel errors. A rate controller is then developed with the objective of maintaining fairness among different videos
while maximizing the received video quality. It is shown that the rate of Compressed Sensed Video (CSV) can be
predictably controlled by varying only the compressed sensing sampling rate. It is then shown that the developed rate
controller can be interpreted as the iterative solution to a convex optimization problem representing the optimization
of the rate allocation across the network. The error resiliency properties of compressed sensed images and videos
are then studied, and an optimal error detection and correction scheme is presented for video transmission over lossy
channels. Finally, the entire system is evaluated through simulation and test bed evaluation. The rate controller is
shown to outperform existing TCP-friendly rate control schemes in terms of both fairness and received video quality.
The test bed results show that the rates converge to stable values in real channels.
26. Steganographic Embedding in JPEG Images with Visual
Criterion.
Synopsis:
In this paper, we present a new information hiding scheme in JPEG images to achieve a
good embedding efficiency considering visual criterion. We construct an embedding impact
model based on human visual system, and then assign each cover element a flipping cost
which would be the key parameter during the embedding procedure. In this way, the
proposed method can minimize the total embedding impact via Viterbi algorithm, meanwhile
improve the visual quality of the stego medium. The experimental results later show that the
proposed information hiding system can perform well in different types of images.
27. As-Projective-As-Possible Image Stitching with Moving
DLT.
Synopsis:
The success of commercial image stitching tools often leads to the impression that image
stitching is a “solved problem”. The reality, however, is that many tools give unconvincing
results when the input photos violate fairly restrictive imaging assumptions; the main two

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being that the photos correspond to views that differ purely by rotation, or that the imaged
scene is effectively planar. Such assumptions underpin the usage of 2D projective
transforms or homographies to align photos. In the hands of the casual user, such
conditions are often violated, yielding misalignment artifacts or “ghosting” in the results.
Accordingly, many existing image stitching tools depend critically on post-processing
routines to conceal ghosting. In this paper, we propose a novel estimation technique called
Moving Direct Linear Transformation (Moving DLT) that is able to tweak or fine-tune the
projective warp to accommodate the deviations of the input data from the idealized
conditions. This produces as-projective-as-possible image alignment that significantly
reduces ghosting without compromising the geometric realism of perspective image
stitching. Our technique thus lessens the dependency on potentially expensive
postprocessing algorithms. In addition, we describe how multiple as-projective-as-possible
warps can be simultaneously refined via bundle adjustment to accurately align multiple
images for large panorama creation.
28. Selecting a Reference High Resolution for Fingerprint
Recognition Using Minutiae and Pores.
Synopsis:
High-resolution automated fingerprint recognition systems (AFRSs) offer higher security
because they are able to make use of level-3 features, such as pores, that are not available
in lower resolution ( <; 500-dpi) images. One of the main parameters affecting the quality of
a digital fingerprint image and issues such as cost, interoperability, and performance of an
AFRS is the choice of image resolution. In this paper, we identify the optimal resolution for
an AFRS using the two most representative fingerprint features: minutiae and pores. We
first designed a multiresolution fingerprint acquisition device to collect fingerprint images at
multiple resolutions and captured fingerprints at various resolutions but at a fixed image
size. We then carried out a theoretical analysis to identify the minimum required resolution
for fingerprint recognition using minutiae and pores. After experiments on our collected
fingerprint images and applying three requirements for the proportions of minutiae and
pores that must be retained in a fingerprint image, we recommend a reference resolution of
800 dpi. Subsequent tests have further confirmed the proposed reference resolution.
29. DEFENSES AGAINST LARGE SCALE ONLINE
PASSWORD GUESSING ATTACKS BY USING
PERSUASIVE CLICK POINTS .
Synopsis:

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Usable security has unique usability challenges because the need for security often means that standard
human-computer-interaction approaches cannot be directly applied. An important usability goal for
authentication systems is to support users in selecting better passwords. Users often create memorable
passwords that are easy for attackers to guess, but strong system-assigned passwords are difficult for
users to remember. So researchers of modern days have gone for alternative methods wherein
graphical pictures are used as passwords. Graphical passwords essentially use images or representation
of images as passwords. Human brain is good in remembering picture than textual character. There are
various graphical password schemes or graphical password software in the market. However, very little
research has been done to analyze graphical passwords that are still immature. There for, this project
work merges persuasive cued click points and password guessing resistant protocol. The major goal of
this work is to reduce the guessing attacks as well as encouraging users to select more random, and
difficult passwords to guess. Well known security threats like brute force attacks and dictionary attacks
can be successfully abolished using this method.
30. Virtualized Screen: A Third Element for CloudMobile
Convergence.
Synopsis:
Mobile and cloud computing have emerged as the new computing platforms and are
converging into a powerful cloud-mobile computing platform. This article envisions a
virtualized screen as a new dimension in such a platform to further optimize the overall
computing experience for users. In a virtualized screen, screen rendering is done in the
cloud, and delivered as images to the client for interactive display. This enables thin-client
mobile devices to enjoy many computationally intensive and graphically rich services.
Technical challenges are discussed and addressed. Two novel cloud-mobile applications,
Cloud Browser and Cloud Phone, are presented to demonstrate the advantages of such a
virtualized screen.
31. Corruptive Artifacts Suppression for Example-Based
Color Transfer.
Synopsis:
Example-based color transfer is a critical operation in image editing but easily suffers from
some corruptive artifacts in the mapping process. In this paper, we propose a novel unified
color transfer framework with corruptive artifacts suppression, which performs iterative
probabilistic color mapping with self-learning filtering scheme and multiscale detail

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manipulation scheme in minimizing the normalized Kullback-Leibler distance. First, an
iterative probabilistic color mapping is applied to construct the mapping relationship
between the reference and target images. Then, a self-learning filtering scheme is applied
into the transfer process to prevent from artifacts and extract details. The transferred output
and the extracted multi-levels details are integrated by the measurement minimization to
yield the final result. Our framework achieves a sound grain suppression, color fidelity and
detail appearance seamlessly. For demonstration, a series of objective and subjective
measurements are used to evaluate the quality in color transfer. Finally, a few extended
applications are implemented to show the applicability of this framework.
32. Visual Object Tracking Based on Local Steering Kernels
and Color Histograms.
Synopsis:
In this paper, we propose a visual object tracking framework, which employs an
appearance-based representation of the target object, based on local steering kernel
descriptors and color histogram information. This framework takes as input the region of the
target object in the previous video frame and a stored instance of the target object, and tries
to localize the object in the current frame by finding the frame region that best resembles
the input. As the object view changes over time, the object model is updated, hence
incorporating these changes. Color histogram similarity between the detected object and
the surrounding background is employed for background subtraction. Experiments are
conducted to test the performance of the proposed framework under various conditions.
The proposed tracking scheme is proven to be successful in tracking objects under scale
and rotation variations and partial occlusion, as well as in tracking rather slowly deformable
articulated objects.
33. Exploring the design space of social network-based Sybil
defenses.
Synopsis:
Recently, there has been significant research interest in leveraging social networks to
defend against Sybil attacks. While much of this work may appear similar at first glance,
existing social network-based Sybil defense schemes can be divided into two categories:
Sybil detection and Sybil tolerance. These two categories of systems both leverage global
properties of the underlying social graph, but they rely on different assumptions and provide
different guarantees: Sybil detection schemes are application-independent and rely only on
the graph structure to identify Sybil identities, while Sybil tolerance schemes rely on
application-specific information and leverage the graph structure and transaction history to

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bound the leverage an attacker can gain from using multiple identities. In this paper, we
take a closer look at the design goals, models, assumptions, guarantees, and limitations of
both categories of social network-based Sybil defense systems.
34. Localization of License Plate Number Using Dynamic
Image Processing Techniques And Genetic Algorithms .
Synopsis:
In this research, the design of a new genetic algorithm (GA) is introduced to detect the
locations of license plate (LP) symbols. An adaptive threshold method is applied to
overcome the dynamic changes of illumination conditions when converting the image into
binary. Connected component analysis technique (CCAT) is used to detect candidate
objects inside the unknown image. A scale-invariant geometric relationship matrix is
introduced to model the layout of symbols in any LP that simplifies system adaptability when
applied in different countries. Moreover, two new crossover operators, based on sorting, are
introduced, which greatly improve the convergence speed of the system. Most of the CCAT
problems, such as touching or broken bodies, are minimized by modifying the GA to
perform partial match until reaching an acceptable fitness value. The system is
implemented using MATLAB and various image samples are experimented with to verify the
distinction of the proposed system. Encouraging results with 98.4% overall accuracy are
reported for two different datasets having variability in orientation, scaling, plate location,
illumination, and complex background. Examples of distorted plate images are successfully
detected due to the independency on the shape, color, or location of the plate.
35. FeatureMatch: A General ANNF Estimation Technique
and its Applications.
Synopsis:
In this paper, we propose FeatureMatch, a generalised approximate nearest-neighbour
field (ANNF) computation framework, between a source and target image. The proposed
algorithm can estimate ANNF maps between any image pairs, not necessarily related. This
generalisation is achieved through appropriate spatial-range transforms. To compute ANNF
maps, global colour adaptation is applied as a range transform on the source image. Image
patches from the pair of images are approximated using low-dimensional features, which
are used along with KD-tree to estimate the ANNF map. This ANNF map is further improved
based on image coherency and spatial transforms. The proposed generalisation, enables
us to handle a wider range of vision applications, which have not been tackled using the

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ANNF framework. We illustrate two such applications namely: 1) optic disk detection and 2)
super resolution. The first application deals with medical imaging, where we locate optic
disks in retinal images using a healthy optic disk image as common target image. The
second application deals with super resolution of synthetic images using a common source
image as dictionary. We make use of ANNF mappings in both these applications and show
experimentally that our proposed approaches are faster and accurate, compared with the
state-of-the-art techniques.
36. Model-Based Edge Detector for Spectral Imagery Using
Sparse Spatiospectral Masks.
Synopsis:
Two model-based algorithms for edge detection in spectral imagery are developed that specifically target
capturing intrinsic features such as isoluminant edges that are characterized by a jump in color but not in
intensity. Given prior knowledge of the classes of reflectance or emittance spectra associated with
candidate objects in a scene, a small set of spectral-band ratios, which most profoundly identify the edge
between each pair of materials, are selected to define a edge signature. The bands that form the edge
signature are fed into a spatial mask, producing a sparse joint spatiospectral nonlinear operator. The first
algorithm achieves edge detection for every material pair by matching the response of the operator at
every pixel with the edge signature for the pair of materials. The second algorithm is a classifier-
enhanced extension of the first algorithm that adaptively accentuates distinctive features before applying
the spatiospectral operator. Both algorithms are extensively verified using spectral imagery from the
airborne hyperspectral imager and from a dots-in-a-well midinfrared imager. In both cases, the multicolor
gradient (MCG) and the hyperspectral/spatial detection of edges (HySPADE) edge detectors are used as
a benchmark for comparison. The results demonstrate that the proposed algorithms outperform the MCG
and HySPADE edge detectors in accuracy, especially when isoluminant edges are present. By requiring
only a few bands as input to the spatiospectral operator, the algorithms enable significant levels of data
compression in band selection. In the presented examples, the required operations per pixel are reduced
by a factor of 71 with respect to those required by the MCG edge detector.
37. Improving Color Constancy by Photometric Edge
Weighting.
Synopsis:
Edge-based color constancy methods make use of image derivatives to estimate the
illuminant. However, different edge types exist in real-world images, such as material,
shadow, and highlight edges. These different edge types may have a distinctive influence
on the performance of the illuminant estimation. Therefore, in this paper, an extensive
analysis is provided of different edge types on the performance of edge-based color

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constancy methods. First, an edge-based taxonomy is presented classifying edge types
based on their photometric properties (e.g., material, shadow-geometry, and highlights).
Then, a performance evaluation of edge-based color constancy is provided using these
different edge types. From this performance evaluation, it is derived that specular and
shadow edge types are more valuable than material edges for the estimation of the
illuminant. To this end, the (iterative) weighted Gray-Edge algorithm is proposed in which
these edge types are more emphasized for the estimation of the illuminant. Images that are
recorded under controlled circumstances demonstrate that the proposed iterative weighted
Gray-Edge algorithm based on highlights reduces the median angular error with
approximately 25 percent. In an uncontrolled environment, improvements in angular error
up to 11 percent are obtained with respect to regular edge-based color constancy.
38. Modeling of Speaking Rate Inﬂuences on Mandarin
Speech Prosody and Its Application to Speaking Rate-
controlled TTS.
Synopsis:
A new data-driven approach to building a speaking rate-dependent hierarchical prosodic
model (SR-HPM), directly from a large prosody-unlabeled speech database containing
utterances of various speaking rates, to describe the influences of speaking rate on
Mandarin speech prosody is proposed. It is an extended version of the existing HPM model
which contains 12 sub-models to describe various relationships of prosodic-acoustic
features of speech signal, linguistic features of the associated text, and prosodic tags
representing the prosodic structure of speech. Two main modifications are suggested. One
is designing proper normalization functions from the statistics of the whole database to
compensate the influences of speaking rate on all prosodic-acoustic features. Another is
modifying the HPM training to let its parameters be speaking-rate dependent. Experimental
results on a large Mandarin read speech corpus showed that the parameters of the SR-
HPM together with these feature normalization functions interpreted the effects of speaking
rate on Mandarin speech prosody very well. An application of the SR-HPM to design and
implement a speaking rate-controlled Mandarin TTS system is demonstrated. The system
can generate natural synthetic speech for any given speaking rate in a wide range of 3.4-
6.8 syllables/sec. Two subjective tests, MOS and preference test, were conducted to
compare the proposed system with the popular HTS system. The MOS scores of the
proposed system were in the range of 3.58-3.83 for eight different speaking rates, while
they were in 3.09-3.43 for HTS. Besides, the proposed system had higher preference
scores (49.8%-79.6%) than those (9.8%-30.7%) of HTS. This confirmed the effectiveness of
the speaking rate control method of the proposed TTS system.

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39. Fingerprint Compression Based on Sparse
Representation.
Synopsis:
A new fingerprint compression algorithm based on sparse representation is introduced.
Obtaining an overcomplete dictionary from a set of fingerprint patches allows us to
represent them as a sparse linear combination of dictionary atoms. In the algorithm, we first
construct a dictionary for predefined fingerprint image patches. For a new given fingerprint
images, represent its patches according to the dictionary by computing l0
-minimization and
then quantize and encode the representation. In this paper, we consider the effect of
various factors on compression results. Three groups of fingerprint images are tested. The
experiments demonstrate that our algorithm is efficient compared with several competing
compression techniques (JPEG, JPEG 2000, and WSQ), especially at high compression
ratios. The experiments also illustrate that the proposed algorithm is robust to extract
minutiae.
40. Ofﬂine Text-Independent Writer Identiﬁcation Based on
Scale Invariant Feature Transform.
Synopsis:
This paper proposes a novel offline text-independent writer identification method based on
scale invariant feature transform (SIFT), composed of training, enrollment, and identification
stages. In all stages, an isotropic LoG filter is first used to segment the handwriting image
into word regions (WRs). Then, the SIFT descriptors (SDs) of WRs and the corresponding
scales and orientations (SOs) are extracted. In the training stage, an SD codebook is
constructed by clustering the SDs of training samples. In the enrollment stage, the SDs of
the input handwriting are adopted to form an SD signature (SDS) by looking up the SD
codebook and the SOs are utilized to generate a scale and orientation histogram (SOH). In
the identification stage, the SDS and SOH of the input handwriting are extracted and
matched with the enrolled ones for identification. Experimental results on six public data
sets (including three English data sets, one Chinese data set, and two hybrid-language data
sets) demonstrate that the proposed method outperforms the state-of-the-art algorithms.
41. IntentSearch:Capturing User Intention for One-Click
Internet Image Search.
Synopsis:

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Web-scale image search engines (e.g., Google image search, Bing image search) mostly
rely on surrounding text features. It is difficult for them to interpret users' search intention
only by query keywords and this leads to ambiguous and noisy search results which are far
from satisfactory. It is important to use visual information in order to solve the ambiguity in
text-based image retrieval. In this paper, we propose a novel Internet image search
approach. It only requires the user to click on one query image with minimum effort and
images from a pool retrieved by text-based search are reranked based on both visual and
textual content. Our key contribution is to capture the users' search intention from this one-
click query image in four steps. 1) The query image is categorized into one of the
predefined adaptive weight categories which reflect users' search intention at a coarse
level. Inside each category, a specific weight schema is used to combine visual features
adaptive to this kind of image to better rerank the text-based search result. 2) Based on the
visual content of the query image selected by the user and through image clustering, query
keywords are expanded to capture user intention. 3) Expanded keywords are used to
enlarge the image pool to contain more relevant images. 4) Expanded keywords are also
used to expand the query image to multiple positive visual examples from which new query
specific visual and textual similarity metrics are learned to further improve content-based
image reranking. All these steps are automatic, without extra effort from the user. This is
critically important for any commercial web-based image search engine, where the user
interface has to be extremely simple. Besides this key contribution, a set of visual features
which are both effective and efficient in Internet image search are designed. Experimental
evaluation shows that our approach significantly improves the precision of top-ranked
images and also the user experi- nce.
42. Photometric Stereo Using Sparse Bayesian Regression
for General Diffuse Surfaces.
Synopsis:
Most conventional algorithms for non-Lambertian photometric stereo can be partitioned into
two categories. The first category is built upon stable outlier rejection techniques while
assuming a dense Lambertian structure for the inliers, and thus performance degrades
when general diffuse regions are present. The second utilizes complex reflectance
representations and non-linear optimization over pixels to handle non-Lambertian surfaces,
but does not explicitly account for shadows or other forms of corrupting outliers. In this
paper, we present a purely pixel-wise photometric stereo method that stably and efficiently
handles various non-Lambertian effects by assuming that appearances can be decomposed
into a sparse, non-diffuse component (e.g., shadows, specularities, etc.) and a diffuse
component represented by a monotonic function of the surface normal and lighting dot-
product. This function is constructed using a piecewise linear approximation to the inverse
diffuse model, leading to closed-form estimates of the surface normals and model
parameters in the absence of non-diffuse corruptions. The latter are modeled as latent

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variables embedded within a hierarchical Bayesian model such that we may accurately
compute the unknown surface normals while simultaneously separating diffuse from non-
diffuse components. Extensive evaluations are performed that show state-of-the-art
performance using both synthetic and real-world images.
43. How to Estimate the Regularization Parameter for
Spectral Regression Discriminant Analysis and its Kernel
Version?
Synopsis:
Spectral regression discriminant analysis (SRDA) has recently been proposed as an
efficient solution to large-scale subspace learning problems. There is a tunable
regularization parameter in SRDA, which is critical to algorithm performance. However, how
to automatically set this parameter has not been well solved until now. So this regularization
parameter was only set to be a constant in SRDA, which is obviously suboptimal. This
paper proposes to automatically estimate the optimal regularization parameter of SRDA
based on the perturbation linear discriminant analysis (PLDA). In addition, two parameter
estimation methods for the kernel version of SRDA are also developed. One is derived from
the method of optimal regularization parameter estimation for SRDA. The other is to utilize
the kernel version of PLDA. Experiments on a number of publicly available databases
demonstrate the effectiveness of the proposed methods for face recognition, spoken letter
recognition, handwritten digit recognition, and text categorization.
44. Quality Assessment of Stereoscopic 3D Image
Compression by Binocular Integration Behaviors.
Synopsis:
The objective approaches of 3D image quality assessment play a key role for the
development of compression standards and various 3D multimedia applications. The quality

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assessment of 3D images faces more new challenges, such as asymmetric stereo
compression, depth perception, and virtual view synthesis, than its 2D counterparts. In
addition, the widely used 2D image quality metrics (e.g., PSNR and SSIM) cannot be
directly applied to deal with these newly introduced challenges. This statement can be
verified by the low correlation between the computed objective measures and the
subjectively measured mean opinion scores (MOSs), when 3D images are the tested
targets. In order to meet these newly introduced challenges, in this paper, besides
traditional 2D image metrics, the binocular integration behaviors-the binocular combination
and the binocular frequency integration, are utilized as the bases for measuring the quality
of stereoscopic 3D images. The effectiveness of the proposed metrics is verified by
conducting subjective evaluations on publicly available stereoscopic image databases.
Experimental results show that significant consistency could be reached between the
measured MOS and the proposed metrics, in which the correlation coefficient between them
can go up to 0.88. Furthermore, we found that the proposed metrics can also address the
quality assessment of the synthesized color-plus-depth 3D images well. Therefore, it is our
belief that the binocular integration behaviors are important factors in the development of
objective quality assessment for 3D images.
45. Robust Face-Name Graph Matching for Movie
Character Identiﬁcation.
Synopsis:
Automatic face identification of characters in movies has drawn significant research
interests and led to many interesting applications. It is a challenging problem due to the
huge variation in the appearance of each character. Although existing methods demonstrate
promising results in clean environment, the performances are limited in complex movie
scenes due to the noises generated during the face tracking and face clustering process. In
this paper we present two schemes of global face-name matching based framework for
robust character identification. The contributions of this work include the following. 1) A
noise insensitive character relationship representation is incorporated. 2) We introduce an
edit operation based graph matching algorithm. 3) Complex character changes are handled
by simultaneously graph partition and graph matching. 4) Beyond existing character
identification approaches, we further perform an in-depth sensitivity analysis by introducing
two types of simulated noises. The proposed schemes demonstrate state-of-the-art
performance on movie character identification in various genres of movies.

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46. Robust Semi-Automatic Depth Map Generation in
Unconstrained Images and Video Sequences for 2D to
Stereoscopic 3D Conversion.
Synopsis:
We describe a system for robustly estimating synthetic depth maps in unconstrained
images and videos, for semi-automatic conversion into stereoscopic 3D. Currently, this
process is automatic or done manually by rotoscopers. Automatic is the least labor
intensive, but makes user intervention or error correction difficult. Manual is the most
accurate, but time consuming and costly. Noting the merits of both, a semi-automatic
method blends them together, allowing for faster and accurate conversion. This requires
user-defined strokes on the image, or over several keyframes for video, corresponding to a
rough estimate of the depths. After, the rest of the depths are determined, creating depth
maps to generate stereoscopic 3D content, with Depth Image Based Rendering to generate
the artificial views. Depth map estimation can be considered as a multi-label segmentation
problem: each class is a depth. For video, we allow the user to label only the first frame,
and we propagate the strokes using computer vision techniques. We combine the merits of
two well-respected segmentation algorithms: Graph Cuts and Random Walks. The diffusion
from Random Walks, with the edge preserving of Graph Cuts should give good results. We
generate good quality content, more suitable for perception, compared to a similar
framework
47. Image Classiﬁcation Using Multiscale Information
Fusion Based on Saliency Driven Nonlinear Diffusion
Filtering.
Synopsis:
In this paper, we propose saliency driven image multiscale nonlinear diffusion filtering. The
resulting scale space in general preserves or even enhances semantically important
structures such as edges, lines, or flow-like structures in the foreground, and inhibits and
smoothes clutter in the background. The image is classified using multiscale information
fusion based on the original image, the image at the final scale at which the diffusion
process converges, and the image at a midscale. Our algorithm emphasizes the foreground
features, which are important for image classification. The background image regions,
whether considered as contexts of the foreground or noise to the foreground, can be

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globally handled by fusing information from different scales. Experimental tests of the
effectiveness of the multiscale space for the image classification are conducted on the
following publicly available datasets: 1) the PASCAL 2005 dataset; 2) the Oxford 102
flowers dataset; and 3) the Oxford 17 flowers dataset, with high classification rates.
48. Sharing Visual Secrets in Single Image Random Dot
Stereograms.
Synopsis:
Visual cryptography schemes (VCSs) generate random and meaningless shares to share
and protect secret images. Conventional VCSs suffer from a transmission risk problem
because the noise-like shares will raise the suspicion of attackers and the attackers might
intercept the transmission. Previous research has involved in hiding shared content in
halftone shares to reduce these risks, but this method exacerbates the pixel expansion
problem and visual quality degradation problem for recovered images. In this paper, a
binocular VCS (BVCS), called the ((2,n)) -BVCS, and an encryption algorithm are proposed
to hide the shared pixels in the single image random dot stereograms (SIRDSs). Because
the SIRDSs have the same 2D appearance as the conventional shares of a VCS, this paper
tries to use SIRDSs as cover images of the shares of VCSs to reduce the transmission risk
of the shares. The encryption algorithm alters the random dots in the SIRDSs according to
the construction rule of the ((2,n)) -BVCS to produce nonpixel-expansion shares of the
BVCS. Altering the dots in a SIRDS will degrade the visual quality of the reconstructed 3D
objects. Hence, we propose an optimization model that is based on the visual quality
requirement of SIRDSs to develop construction rules for a ((2,n)) -BVCS that maximize the
contrast of the recovered image in the BVCS.
49. Robust Watermarking of Compressed and Encrypted
JPEG2000 Images.
Synopsis:
Digital asset management systems (DAMS) generally handle media data in a compressed
and encrypted form. It is sometimes necessary to watermark these compressed encrypted
media items in the compressed-encrypted domain itself for tamper detection or ownership
declaration or copyright management purposes. It is a challenge to watermark these
compressed encrypted streams as the compression process would have packed the
information of raw media into a low number of bits and encryption would have randomized
the compressed bit stream. Attempting to watermark such a randomized bit stream can
cause a dramatic degradation of the media quality. Thus it is necessary to choose an
encryption scheme that is both secure and will allow watermarking in a predictable manner

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in the compressed encrypted domain. In this paper, we propose a robust watermarking
algorithm to watermark JPEG2000 compressed and encrypted images. The encryption
algorithm we propose to use is a stream cipher. While the proposed technique embeds
watermark in the compressed-encrypted domain, the extraction of watermark can be done
in the decrypted domain. We investigate in detail the embedding capacity, robustness,
perceptual quality and security of the proposed algorithm, using these watermarking
schemes: Spread Spectrum (SS), Scalar Costa Scheme Quantization Index Modulation
(SCS-QIM), and Rational Dither Modulation (RDM).
50. Single-Image Superresolution of Natural Stochastic
Textures Based on Fractional Brownian Motion.
Synopsis:
Texture enhancement presents an ongoing challenge, in spite of the considerable progress
made in recent years. Whereas most of the effort has been devoted so far to enhancement
of regular textures, stochastic textures that are encountered in most natural images, still
pose an outstanding problem. The purpose of enhancement of stochastic textures is to
recover details, which were lost during the acquisition of the image. In this paper, a texture
model, based on fractional Brownian motion (fBm), is proposed. The model is global and
does not entail using image patches. The fBm is a self-similar stochastic process. Self-
similarity is known to characterize a large class of natural textures. The fBm-based model is
evaluated and a single-image regularized superresolution algorithm is derived. The
proposed algorithm is useful for enhancement of a wide range of textures. Its performance
is compared with single-image superresolution methods and its advantages are highlighted.
51. LBP-Based Edge-Texture Features for Object
Recognition.
Synopsis:
This paper proposes two sets of novel edge-texture features, Discriminative Robust Local
Binary Pattern (DRLBP) and Ternary Pattern (DRLTP), for object recognition. By
investigating the limitations of Local Binary Pattern (LBP), Local Ternary Pattern (LTP) and
Robust LBP (RLBP), DRLBP and DRLTP are proposed as new features. They solve the
problem of discrimination between a bright object against a dark background and vice-versa

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inherent in LBP and LTP. DRLBP also resolves the problem of RLBP whereby LBP codes
and their complements in the same block are mapped to the same code. Furthermore, the
proposed features retain contrast information necessary for proper representation of object
contours that LBP, LTP, and RLBP discard. Our proposed features are tested on seven
challenging data sets: INRIA Human, Caltech Pedestrian, UIUC Car, Caltech 101, Caltech
256, Brodatz, and KTH-TIPS2-a. Results demonstrate that the proposed features
outperform the compared approaches on most data sets.
52. View-invariant action recognition based on Artificial
Neural Networks .
Synopsis:
In this paper, a novel view invariant action recognition method based on neural network
representation and recognition is proposed. The novel representation of action videos is
based on learning spatially related human body posture prototypes using self organizing
maps. Fuzzy distances from human body posture prototypes are used to produce a time
invariant action representation. Multilayer perceptrons are used for action classification. The
algorithm is trained using data from a multi-camera setup. An arbitrary number of cameras
can be used in order to recognize actions using a Bayesian framework. The proposed
method can also be applied to videos depicting interactions between humans, without any
modification. The use of information captured from different viewing angles leads to high
classification performance. The proposed method is the first one that has been tested in
challenging experimental setups, a fact that denotes its effectiveness to deal with most of
the open issues in action recognition.
53. Semisupervised Biased Maximum Margin Analysis for
Interactive Image Retrieval.
Synopsis:
With many potential practical applications, content-based image retrieval (CBIR) has
attracted substantial attention during the past few years. A variety of relevance feedback
(RF) schemes have been developed as a powerful tool to bridge the semantic gap between
low-level visual features and high-level semantic concepts, and thus to improve the
performance of CBIR systems. Among various RF approaches, support-vector-machine
(SVM)-based RF is one of the most popular techniques in CBIR. Despite the success,
directly using SVM as an RF scheme has two main drawbacks. First, it treats the positive
and negative feedbacks equally, which is not appropriate since the two groups of training
feedbacks have distinct properties. Second, most of the SVM-based RF techniques do not
take into account the unlabeled samples, although they are very helpful in constructing a

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good classifier. To explore solutions to overcome these two drawbacks, in this paper, we
propose a biased maximum margin analysis (BMMA) and a semisupervised BMMA
(SemiBMMA) for integrating the distinct properties of feedbacks and utilizing the information
of unlabeled samples for SVM-based RF schemes. The BMMA differentiates positive
feedbacks from negative ones based on local analysis, whereas the SemiBMMA can
effectively integrate information of unlabeled samples by introducing a Laplacian regularizer
to the BMMA. We formally formulate this problem into a general subspace learning task and
then propose an automatic approach of determining the dimensionality of the embedded
subspace for RF. Extensive experiments on a large real-world image database demonstrate
that the proposed scheme combined with the SVM RF can significantly improve the
performance of CBIR systems.
54. User-aware Image Tag Reﬁnement via Ternary Semantic
Analysis.
Synopsis:
Large-scale user contributed images with tags are easily available on photo sharing
websites. However, the noisy or incomplete correspondence between the images and tags
prohibits them from being leveraged for precise image retrieval and effective management.
To tackle the problem of tag refinement, we propose a method of Ranking based Multi-
correlation Tensor Factorization (RMTF), to jointly model the ternary relations among user,
image, and tag, and further to precisely reconstruct the user-aware image-tag associations
as a result. Since the user interest or background can be explored to eliminate the
ambiguity of image tags, the proposed RMTF is believed to be superior to the traditional
solutions, which only focus on the binary image-tag relations. During the model estimation,
we employ a ranking based optimization scheme to interpret the tagging data, in which the
pair-wise qualitative difference between positive and negative examples is used, instead of
the point-wise 0/1 confidence. Specifically, the positive examples are directly decided by the
observed user-image-tag interrelations, while the negative ones are collected with respect
to the most semantically and contextually irrelevant tags. Extensive experiments on a
benchmark Flickr dataset demonstrate the effectiveness of the proposed solution for tag
refinement. We also show attractive performances on two potential applications as the by-
products of the ternary relation analysis.
55. Learning Layouts for Single-Page Graphic Designs.
Synopsis:

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This paper presents an approach for automatically creating graphic design layouts using a new energy-based model
derived from design principles. The model includes several new algorithms for analyzing graphic designs, including
the prediction of perceived importance, alignment detection, and hierarchical segmentation. Given the model, we use
optimization to synthesize new layouts for a variety of single-page graphic designs. Model parameters are learned
with Nonlinear Inverse Optimization (NIO) from a small number of example layouts. To demonstrate our approach,
we show results for applications including generating design layouts in various styles, retargeting designs to new
sizes, and improving existing designs. We also compare our automatic results with designs created using
crowdsourcing and show that our approach performs slightly better than novice designers.
56. Separable Reversible Data Hiding in Encrypted Image.
Synopsis:
This work proposes a novel scheme for separable reversible data hiding in encrypted
images. In the first phase, a content owner encrypts the original uncompressed image using
an encryption key. Then, a data-hider may compress the least significant bits of the
encrypted image using a data-hiding key to create a sparse space to accommodate some
additional data. With an encrypted image containing additional data, if a receiver has the
data-hiding key, he can extract the additional data though he does not know the image
content. If the receiver has the encryption key, he can decrypt the received data to obtain
an image similar to the original one, but cannot extract the additional data. If the receiver
has both the data-hiding key and the encryption key, he can extract the additional data and
recover the original content without any error by exploiting the spatial correlation in natural
image when the amount of additional data is not too large.
57. Toward Experiential Mobile Media Processing.
Synopsis:
Smartphone cameras are often used to take pictures and videos because so many people
now carry their smartphones everywhere. Therefore, smartphone camera quality plays an
important role in overall smartphone evaluation and usage. Although smartphone camera
quality has been continually improving, mobile media processing can provide significant
enhancements. The authors discuss mobile media processing techniques, including color
and contrast enhancement, backlight image compensation, deblurring, denoising, and video
stabilization, within the framework of smartphone camera quality improvement.
58. Weighted KPCA Degree of Homogeneity Amended
Nonclassical Receptive Field Inhibition Model for Salient
Contour Extraction in Low-Light-Level Image

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Synopsis:
The stimulus response of the classical receptive field (CRF) of neuron in primary visual
cortex is affected by its periphery [i.e., non-CRF (nCRF)]. This modulation exerts inhibition,
which depends primarily on the correlation of both visual stimulations. The theory of
periphery and center interaction with visual characteristics can be applied in night vision
information processing. In this paper, a weighted kernel principal component analysis
(WKPCA) degree of homogeneity (DH) amended inhibition model inspired by visual
perceptual mechanisms is proposed to extract salient contour from complex natural scene
in low-light-level image. The core idea is that multifeature analysis can recognize the
homogeneity in modulation coverage effectively. Computationally, a novel WKPCA
algorithm is presented to eliminate outliers and anomalous distribution in CRF and
accomplish principal component analysis precisely. On this basis, a new concept and
computational procedure for DH is defined to evaluate the dissimilarity between periphery
and center comprehensively. Through amending the inhibition from nCRF to CRF by DH,
our model can reduce the interference of noises, suppress details, and textures in
homogeneous regions accurately. It helps to further avoid mutual suppression among
inhomogeneous regions and contour elements. This paper provides an improved
computational visual model with high-performance for contour detection from cluttered
natural scene in night vision image.

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