pwc-archive/papers-with-abstracts huggingface dataset:
Hiding Image in Image by Five Modulus Method for Image Steganography
Authors: Firas A. Jassim
Date: 2013-04-04 00:00:00
Tasks: Image Steganography

This paper is to create a practical steganographic implementation to hide
color image (stego) inside another color image (cover). The proposed technique
uses Five Modulus Method to convert the whole pixels within both the cover and
the stego images into multiples of five. Since each pixels inside the stego
image is divisible by five then the whole stego image could be divided by five
to get new range of pixels 0..51. Basically, the reminder of each number that
is not divisible by five is either 1,2,3 or 4 when divided by 5. Subsequently,
then a 4-by-4 window size has been implemented to accommodate the proposed
technique. For each 4-by-4 window inside the cover image, a number from 1 to 4
could be embedded secretly from the stego image. The previous discussion must
be applied separately for each of the R, G, and B arrays. Moreover, a stego-key
could be combined with the proposed algorithm to make it difficult for any
adversary to extract the secret image from the cover image. Based on the PSNR
value, the extracted stego image has high PSNR value. Hence this new
steganography algorithm is very efficient to hide color images.
Multiscale Fractal Descriptors Applied to Texture Classification
Authors: João Batista Florindo, Odemir Martinez Bruno
Date: 2013-04-04 00:00:00
Tasks: Classification, General Classification, Texture Classification

This work proposes the combination of multiscale transform with fractal
descriptors employed in the classification of gray-level texture images. We
apply the space-scale transform (derivative + Gaussian filter) over the
Bouligand-Minkowski fractal descriptors, followed by a threshold over the
filter response, aiming at attenuating noise effects caused by the final part
of this response. The method is tested in the classification of a well-known
data set (Brodatz) and compared with other classical texture descriptor
techniques. The results demonstrate the advantage of the proposed approach,
achieving a higher success rate with a reduced amount of descriptors.
Integration of spatio-temporal contrast sensitivity with a multi-slice channelized Hotelling observer
Authors: Ali N. Avanaki, Kathryn S. Espig, Cedric Marchessoux, Elizabeth A. Krupinski, Predrag R. Bakic, Tom R. L. Kimpe, Andrew D. A. Maidment
Date: 2013-04-04 00:00:00
Tasks: Sensitivity

Barten's model of spatio-temporal contrast sensitivity function of human
visual system is embedded in a multi-slice channelized Hotelling observer. This
is done by 3D filtering of the stack of images with the spatio-temporal
contrast sensitivity function and feeding the result (i.e., the perceived image
stack) to the multi-slice channelized Hotelling observer. The proposed
procedure of considering spatio-temporal contrast sensitivity function is
generic in the sense that it can be used with observers other than multi-slice
channelized Hotelling observer. Detection performance of the new observer in
digital breast tomosynthesis is measured in a variety of browsing speeds, at
two spatial sampling rates, using computer simulations. Our results show a peak
in detection performance in mid browsing speeds. We compare our results to
those of a human observer study reported earlier (I. Diaz et al. SPIE MI 2011).
The effects of display luminance, contrast and spatial sampling rate, with and
without considering foveal vision, are also studied. Reported simulations are
conducted with real digital breast tomosynthesis image stacks, as well as
stacks from an anthropomorphic software breast phantom (P. Bakic et al. Med
Phys. 2011). Lesion cases are simulated by inserting single
micro-calcifications or masses. Limitations of our methods and ways to improve
them are discussed.
Restoration of Images Corrupted by Impulse Noise and Mixed Gaussian Impulse Noise using Blind Inpainting
Authors: Ming Yan
Date: 2013-04-04 00:00:00
Tasks: Image Restoration

This article studies the problem of image restoration of observed images
corrupted by impulse noise and mixed Gaussian impulse noise. Since the pixels
damaged by impulse noise contain no information about the true image, how to
find this set correctly is a very important problem. We propose two methods
based on blind inpainting and $\ell_0$ minimization that can simultaneously
find the damaged pixels and restore the image. By iteratively restoring the
image and updating the set of damaged pixels, these methods have better
performance than other methods, as shown in the experiments. In addition, we
provide convergence analysis for these methods, these algorithms will converge
to coordinatewise minimum points. In addition, they will converge to local
minimum points (or with probability one) with some modifications in the
algorithms.
Fast ALS-based tensor factorization for context-aware recommendation from implicit feedback
Authors: Balázs Hidasi, Domonkos Tikk
Date: 2012-04-05 00:00:00
Tasks: Computational Efficiency

Albeit, the implicit feedback based recommendation problem - when only the
user history is available but there are no ratings - is the most typical
setting in real-world applications, it is much less researched than the
explicit feedback case. State-of-the-art algorithms that are efficient on the
explicit case cannot be straightforwardly transformed to the implicit case if
scalability should be maintained. There are few if any implicit feedback
benchmark datasets, therefore new ideas are usually experimented on explicit
benchmarks. In this paper, we propose a generic context-aware implicit feedback
recommender algorithm, coined iTALS. iTALS apply a fast, ALS-based tensor
factorization learning method that scales linearly with the number of non-zero
elements in the tensor. The method also allows us to incorporate diverse
context information into the model while maintaining its computational
efficiency. In particular, we present two such context-aware implementation
variants of iTALS. The first incorporates seasonality and enables to
distinguish user behavior in different time intervals. The other views the user
history as sequential information and has the ability to recognize usage
pattern typical to certain group of items, e.g. to automatically tell apart
product types or categories that are typically purchased repetitively
(collectibles, grocery goods) or once (household appliances). Experiments
performed on three implicit datasets (two proprietary ones and an implicit
variant of the Netflix dataset) show that by integrating context-aware
information with our factorization framework into the state-of-the-art implicit
recommender algorithm the recommendation quality improves significantly.
Fast SVM training using approximate extreme points
Authors: Manu Nandan, Pramod P. Khargonekar, Sachin S. Talathi
Date: 2013-04-04 00:00:00
Tasks: General Classification, Seizure Detection

Applications of non-linear kernel Support Vector Machines (SVMs) to large
datasets is seriously hampered by its excessive training time. We propose a
modification, called the approximate extreme points support vector machine
(AESVM), that is aimed at overcoming this burden. Our approach relies on
conducting the SVM optimization over a carefully selected subset, called the
representative set, of the training dataset. We present analytical results that
indicate the similarity of AESVM and SVM solutions. A linear time algorithm
based on convex hulls and extreme points is used to compute the representative
set in kernel space. Extensive computational experiments on nine datasets
compared AESVM to LIBSVM \citep{LIBSVM}, CVM \citep{Tsang05}, BVM
\citep{Tsang07}, LASVM \citep{Bordes05}, $\text{SVM}^{\text{perf}}$
\citep{Joachims09}, and the random features method \citep{rahimi07}. Our AESVM
implementation was found to train much faster than the other methods, while its
classification accuracy was similar to that of LIBSVM in all cases. In
particular, for a seizure detection dataset, AESVM training was almost $10^3$
times faster than LIBSVM and LASVM and more than forty times faster than CVM
and BVM. Additionally, AESVM also gave competitively fast classification times.
Stochastic model for the vocabulary growth in natural languages
Authors: Martin Gerlach, Eduardo G. Altmann
Date: 2012-12-06 00:00:00
Tasks: Descriptive

We propose a stochastic model for the number of different words in a given
database which incorporates the dependence on the database size and historical
changes. The main feature of our model is the existence of two different
classes of words: (i) a finite number of core-words which have higher frequency
and do not affect the probability of a new word to be used; and (ii) the
remaining virtually infinite number of noncore-words which have lower frequency
and once used reduce the probability of a new word to be used in the future.
Our model relies on a careful analysis of the google-ngram database of books
published in the last centuries and its main consequence is the generalization
of Zipf's and Heaps' law to two scaling regimes. We confirm that these
generalizations yield the best simple description of the data among generic
descriptive models and that the two free parameters depend only on the language
but not on the database. From the point of view of our model the main change on
historical time scales is the composition of the specific words included in the
finite list of core-words, which we observe to decay exponentially in time with
a rate of approximately 30 words per year for English.
Classification of Human Epithelial Type 2 Cell Indirect Immunofluoresence Images via Codebook Based Descriptors
Authors: Arnold Wiliem, Yongkang Wong, Conrad Sanderson, Peter Hobson, Shaokang Chen, Brian C. Lovell
Date: 2013-04-04 00:00:00
Tasks: Diagnostic, General Classification

The Anti-Nuclear Antibody (ANA) clinical pathology test is commonly used to
identify the existence of various diseases. A hallmark method for identifying
the presence of ANAs is the Indirect Immunofluorescence method on Human
Epithelial (HEp-2) cells, due to its high sensitivity and the large range of
antigens that can be detected. However, the method suffers from numerous
shortcomings, such as being subjective as well as time and labour intensive.
Computer Aided Diagnostic (CAD) systems have been developed to address these
problems, which automatically classify a HEp-2 cell image into one of its known
patterns (eg., speckled, homogeneous). Most of the existing CAD systems use
handpicked features to represent a HEp-2 cell image, which may only work in
limited scenarios. In this paper, we propose a cell classification system
comprised of a dual-region codebook-based descriptor, combined with the Nearest
Convex Hull Classifier. We evaluate the performance of several variants of the
descriptor on two publicly available datasets: ICPR HEp-2 cell classification
contest dataset and the new SNPHEp-2 dataset. To our knowledge, this is the
first time codebook-based descriptors are applied and studied in this domain.
Experiments show that the proposed system has consistent high performance and
is more robust than two recent CAD systems.
Fast Approximate L_infty Minimization: Speeding Up Robust Regression
Authors: Fumin Shen, Chunhua Shen, Rhys Hill, Anton Van Den Hengel, Zhenmin Tang
Date: 2013-04-04 00:00:00
Tasks: regression

Minimization of the $L_\infty$ norm, which can be viewed as approximately
solving the non-convex least median estimation problem, is a powerful method
for outlier removal and hence robust regression. However, current techniques
for solving the problem at the heart of $L_\infty$ norm minimization are slow,
and therefore cannot scale to large problems. A new method for the minimization
of the $L_\infty$ norm is presented here, which provides a speedup of multiple
orders of magnitude for data with high dimension. This method, termed Fast
$L_\infty$ Minimization, allows robust regression to be applied to a class of
problems which were previously inaccessible. It is shown how the $L_\infty$
norm minimization problem can be broken up into smaller sub-problems, which can
then be solved extremely efficiently. Experimental results demonstrate the
radical reduction in computation time, along with robustness against large
numbers of outliers in a few model-fitting problems.
Shadow Detection: A Survey and Comparative Evaluation of Recent Methods
Authors: Andres Sanin, Conrad Sanderson, Brian C. Lovell
Date: 2013-04-04 00:00:00
Tasks: object-detection, Object Detection, Shadow Detection, Shadow Removal, Survey

This paper presents a survey and a comparative evaluation of recent
techniques for moving cast shadow detection. We identify shadow removal as a
critical step for improving object detection and tracking. The survey covers
methods published during the last decade, and places them in a feature-based
taxonomy comprised of four categories: chromacity, physical, geometry and
textures. A selection of prominent methods across the categories is compared in
terms of quantitative performance measures (shadow detection and discrimination
rates, colour desaturation) as well as qualitative observations. Furthermore,
we propose the use of tracking performance as an unbiased approach for
determining the practical usefulness of shadow detection methods. The
evaluation indicates that all shadow detection approaches make different
contributions and all have individual strength and weaknesses. Out of the
selected methods, the geometry-based technique has strict assumptions and is
not generalisable to various environments, but it is a straightforward choice
when the objects of interest are easy to model and their shadows have different
orientation. The chromacity based method is the fastest to implement and run,
but it is sensitive to noise and less effective in low saturated scenes. The
physical method improves upon the accuracy of the chromacity method by adapting
to local shadow models, but fails when the spectral properties of the objects
are similar to that of the background. The small-region texture based method is
especially robust for pixels whose neighbourhood is textured, but may take
longer to implement and is the most computationally expensive. The large-region
texture based method produces the most accurate results, but has a significant
computational load due to its multiple processing steps.
Highly comparative time-series analysis: The empirical structure of time series and their methods
Authors: Ben D. Fulcher, Max A. Little, Nick S. Jones
Date: 2013-04-03 00:00:00
Proceeding: Journal of the Royal Society Interface 2013 6
Tasks: Time Series, Time Series Analysis, Time Series Classification

The process of collecting and organizing sets of observations represents a
common theme throughout the history of science. However, despite the ubiquity
of scientists measuring, recording, and analyzing the dynamics of different
processes, an extensive organization of scientific time-series data and
analysis methods has never been performed. Addressing this, annotated
collections of over 35 000 real-world and model-generated time series and over
9000 time-series analysis algorithms are analyzed in this work. We introduce
reduced representations of both time series, in terms of their properties
measured by diverse scientific methods, and of time-series analysis methods, in
terms of their behaviour on empirical time series, and use them to organize
these interdisciplinary resources. This new approach to comparing across
diverse scientific data and methods allows us to organize time-series datasets
automatically according to their properties, retrieve alternatives to
particular analysis methods developed in other scientific disciplines, and
automate the selection of useful methods for time-series classification and
regression tasks. The broad scientific utility of these tools is demonstrated
on datasets of electroencephalograms, self-affine time series, heart beat
intervals, speech signals, and others, in each case contributing novel analysis
techniques to the existing literature. Highly comparative techniques that
compare across an interdisciplinary literature can thus be used to guide more
focused research in time-series analysis for applications across the scientific
disciplines.
Efficient Distance Metric Learning by Adaptive Sampling and Mini-Batch Stochastic Gradient Descent (SGD)
Authors: Qi Qian, Rong Jin, Jin-Feng Yi, Lijun Zhang, Shenghuo Zhu
Date: 2013-04-03 00:00:00
Tasks: Computational Efficiency, Metric Learning

Distance metric learning (DML) is an important task that has found
applications in many domains. The high computational cost of DML arises from
the large number of variables to be determined and the constraint that a
distance metric has to be a positive semi-definite (PSD) matrix. Although
stochastic gradient descent (SGD) has been successfully applied to improve the
efficiency of DML, it can still be computationally expensive because in order
to ensure that the solution is a PSD matrix, it has to, at every iteration,
project the updated distance metric onto the PSD cone, an expensive operation.
We address this challenge by developing two strategies within SGD, i.e.
mini-batch and adaptive sampling, to effectively reduce the number of updates
(i.e., projections onto the PSD cone) in SGD. We also develop hybrid approaches
that combine the strength of adaptive sampling with that of mini-batch online
learning techniques to further improve the computational efficiency of SGD for
DML. We prove the theoretical guarantees for both adaptive sampling and
mini-batch based approaches for DML. We also conduct an extensive empirical
study to verify the effectiveness of the proposed algorithms for DML.
Predator confusion is sufficient to evolve swarming behavior
Authors: Randal S. Olson, Arend Hintze, Fred C. Dyer, David B. Knoester, Christoph Adami
Date: 2012-09-14 00:00:00

Swarming behaviors in animals have been extensively studied due to their
implications for the evolution of cooperation, social cognition, and
predator-prey dynamics. An important goal of these studies is discerning which
evolutionary pressures favor the formation of swarms. One hypothesis is that
swarms arise because the presence of multiple moving prey in swarms causes
confusion for attacking predators, but it remains unclear how important this
selective force is. Using an evolutionary model of a predator-prey system, we
show that predator confusion provides a sufficient selection pressure to evolve
swarming behavior in prey. Furthermore, we demonstrate that the evolutionary
effect of predator confusion on prey could in turn exert pressure on the
structure of the predator's visual field, favoring the frontally oriented,
high-resolution visual systems commonly observed in predators that feed on
swarming animals. Finally, we provide evidence that when prey evolve swarming
in response to predator confusion, there is a change in the shape of the
functional response curve describing the predator's consumption rate as prey
density increases. Thus, we show that a relatively simple perceptual
constraint--predator confusion--could have pervasive evolutionary effects on
prey behavior, predator sensory mechanisms, and the ecological interactions
between predators and prey.
Heteroscedastic Conditional Ordinal Random Fields for Pain Intensity Estimation from Facial Images
Authors: Ognjen Rudovic, Maja Pantic, Vladimir Pavlovic
Date: 2013-01-22 00:00:00
Tasks: General Classification, regression

We propose a novel method for automatic pain intensity estimation from facial
images based on the framework of kernel Conditional Ordinal Random Fields
(KCORF). We extend this framework to account for heteroscedasticity on the
output labels(i.e., pain intensity scores) and introduce a novel dynamic
features, dynamic ranks, that impose temporal ordinal constraints on the static
ranks (i.e., intensity scores). Our experimental results show that the proposed
approach outperforms state-of-the art methods for sequence classification with
ordinal data and other ordinal regression models. The approach performs
significantly better than other models in terms of Intra-Class Correlation
measure, which is the most accepted evaluation measure in the tasks of facial
behaviour intensity estimation.
A software for aging faces applied to ancient marble busts
Authors: Amelia Carolina Sparavigna
Date: 2013-04-03 00:00:00
Tasks: Face Recognition

The study and development of software able to show the effect of aging of
faces is one of the tasks of face recognition technologies. Some software
solutions are used for investigations, some others to show the effects of drugs
on healthy appearance, however some other applications can be proposed for the
analysis of visual arts. Here we use a freely available software, which is
providing interesting results, for the comparison of ancient marble busts. An
analysis of Augustus busts is proposed.
Scale Selection of Adaptive Kernel Regression by Joint Saliency Map for Nonrigid Image Registration
Authors: Zhuangming Shen, Jiuai Sun, HUI ZHANG, Binjie Qin
Date: 2013-03-03 00:00:00
Tasks: Image Registration, regression

Joint saliency map (JSM) [1] was developed to assign high joint saliency
values to the corresponding saliency structures (called Joint Saliency
Structures, JSSs) but zero or low joint saliency values to the outliers (or
mismatches) that are introduced by missing correspondence or local large
deformations between the reference and moving images to be registered. JSM
guides the local structure matching in nonrigid registration by emphasizing
these JSSs' sparse deformation vectors in adaptive kernel regression of
hierarchical sparse deformation vectors for iterative dense deformation
reconstruction. By designing an effective superpixel-based local structure
scale estimator to compute the reference structure's structure scale, we
further propose to determine the scale (the width) of kernels in the adaptive
kernel regression through combining the structure scales to JSM-based scales of
mismatch between the local saliency structures. Therefore, we can adaptively
select the sample size of sparse deformation vectors to reconstruct the dense
deformation vectors for accurately matching the every local structures in the
two images. The experimental results demonstrate better accuracy of our method
in aligning two images with missing correspondence and local large deformation
than the state-of-the-art methods.
Transferring Subspaces Between Subjects in Brain-Computer Interfacing
Authors: Wojciech Samek, Frank C. Meinecke, Klaus-Robert Müller
Date: 2012-09-18 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Motor Imagery

Compensating changes between a subjects' training and testing session in
Brain Computer Interfacing (BCI) is challenging but of great importance for a
robust BCI operation. We show that such changes are very similar between
subjects, thus can be reliably estimated using data from other users and
utilized to construct an invariant feature space. This novel approach to
learning from other subjects aims to reduce the adverse effects of common
non-stationarities, but does not transfer discriminative information. This is
an important conceptual difference to standard multi-subject methods that e.g.
improve the covariance matrix estimation by shrinking it towards the average of
other users or construct a global feature space. These methods do not reduces
the shift between training and test data and may produce poor results when
subjects have very different signal characteristics. In this paper we compare
our approach to two state-of-the-art multi-subject methods on toy data and two
data sets of EEG recordings from subjects performing motor imagery. We show
that it can not only achieve a significant increase in performance, but also
that the extracted change patterns allow for a neurophysiologically meaningful
interpretation.