pwc-archive/papers-with-abstracts huggingface dataset:
Human Face Recognition using Gabor based Kernel Entropy Component Analysis
Authors: Arindam Kar, Debotosh Bhattacharjee, Dipak Kumar Basu, Mita Nasipuri, Mahantapas Kundu
Date: 2013-12-05 00:00:00
Tasks: Face Recognition, image-classification, Image Classification

In this paper, we present a novel Gabor wavelet based Kernel Entropy Component Analysis (KECA) method by integrating the Gabor wavelet transformation (GWT) of facial images with the KECA method for enhanced face recognition performance. Firstly, from the Gabor wavelet transformed images the most important discriminative desirable facial features characterized by spatial frequency, spatial locality and orientation selectivity to cope with the variations due to illumination and facial expression changes were derived. After that KECA, relating to the Renyi entropy is extended to include cosine kernel function. The KECA with the cosine kernels is then applied on the extracted most important discriminating feature vectors of facial images to obtain only those real kernel ECA eigenvectors that are associated with eigenvalues having positive entropy contribution. Finally, these real KECA features are used for image classification using the L1, L2 distance measures; the Mahalanobis distance measure and the cosine similarity measure. The feasibility of the Gabor based KECA method with the cosine kernel has been successfully tested on both frontal and pose-angled face recognition, using datasets from the ORL, FRAV2D and the FERET database.
A Face Recognition approach based on entropy estimate of the nonlinear DCT features in the Logarithm Domain together with Kernel Entropy Component Analysis
Authors: Arindam Kar, Debotosh Bhattacharjee, Dipak Kumar Basu, Mita Nasipuri, Mahantapas Kundu
Date: 2013-12-05 00:00:00
Tasks: ARC, Face Recognition, Specificity

This paper exploits the feature extraction capabilities of the discrete cosine transform (DCT) together with an illumination normalization approach in the logarithm domain that increase its robustness to variations in facial geometry and illumination. Secondly in the same domain the entropy measures are applied on the DCT coefficients so that maximum entropy preserving pixels can be extracted as the feature vector. Thus the informative features of a face can be extracted in a low dimensional space. Finally, the kernel entropy component analysis (KECA) with an extension of arc cosine kernels is applied on the extracted DCT coefficients that contribute most to the entropy estimate to obtain only those real kernel ECA eigenvectors that are associated with eigenvalues having high positive entropy contribution. The resulting system was successfully tested on real image sequences and is robust to significant partial occlusion and illumination changes, validated with the experiments on the FERET, AR, FRAV2D and ORL face databases. Experimental comparison is demonstrated to prove the superiority of the proposed approach in respect to recognition accuracy. Using specificity and sensitivity we find that the best is achieved when Renyi entropy is applied on the DCT coefficients. Extensive experimental comparison is demonstrated to prove the superiority of the proposed approach in respect to recognition accuracy. Moreover, the proposed approach is very simple, computationally fast and can be implemented in any real-time face recognition system.
High Performance Human Face Recognition using Gabor based Pseudo Hidden Markov Model
Authors: Arindam Kar, Debotosh Bhattacharjee, Dipak Kumar Basu, Mita Nasipuri, Mahantapas Kundu
Date: 2013-12-05 00:00:00
Tasks: Face Recognition, feature selection

This paper introduces a novel methodology that combines the multi-resolution feature of the Gabor wavelet transformation (GWT) with the local interactions of the facial structures expressed through the Pseudo Hidden Markov model (PHMM). Unlike the traditional zigzag scanning method for feature extraction a continuous scanning method from top-left corner to right then top-down and right to left and so on until right-bottom of the image i.e. a spiral scanning technique has been proposed for better feature selection. Unlike traditional HMMs, the proposed PHMM does not perform the state conditional independence of the visible observation sequence assumption. This is achieved via the concept of local structures introduced by the PHMM used to extract facial bands and automatically select the most informative features of a face image. Thus, the long-range dependency problem inherent to traditional HMMs has been drastically reduced. Again with the use of most informative pixels rather than the whole image makes the proposed method reasonably faster for face recognition. This method has been successfully tested on frontal face images from the ORL, FRAV2D and FERET face databases where the images vary in pose, illumination, expression, and scale. The FERET data set contains 2200 frontal face images of 200 subjects, while the FRAV2D data set consists of 1100 images of 100 subjects and the full ORL database is considered. The results reported in this application are far better than the recent and most referred systems.
A Gabor block based Kernel Discriminative Common Vector (KDCV) approach using cosine kernels for Human Face Recognition
Authors: Arindam Kar, Debotosh Bhattacharjee, Dipak Kumar Basu, Mita Nasipuri, Mahantapas Kundu
Date: 2013-12-05 00:00:00
Tasks: Face Recognition, image-classification, Image Classification

In this paper a nonlinear Gabor Wavelet Transform (GWT) discriminant feature extraction approach for enhanced face recognition is proposed. Firstly, the low-energized blocks from Gabor wavelet transformed images are extracted. Secondly, the nonlinear discriminating features are analyzed and extracted from the selected low-energized blocks by the generalized Kernel Discriminative Common Vector (KDCV) method. The KDCV method is extended to include cosine kernel function in the discriminating method. The KDCV with the cosine kernels is then applied on the extracted low energized discriminating feature vectors to obtain the real component of a complex quantity for face recognition. In order to derive positive kernel discriminative vectors; we apply only those kernel discriminative eigenvectors that are associated with non-zero eigenvalues. The feasibility of the low energized Gabor block based generalized KDCV method with cosine kernel function models has been successfully tested for image classification using the L1, L2 distance measures; and the cosine similarity measure on both frontal and pose-angled face recognition. Experimental results on the FRAV2D and the FERET database demonstrate the effectiveness of this new approach.
Face Recognition using Hough Peaks extracted from the significant blocks of the Gradient Image
Authors: Arindam Kar, Debotosh Bhattacharjee, Dipak Kumar Basu, Mita Nasipuri, Mahantapas Kundu
Date: 2013-12-05 00:00:00
Tasks: Dimensionality Reduction, Face Recognition

This paper proposes a new technique for automatic face recognition using integrated peaks of the Hough transformed significant blocks of the binary gradient image. In this approach firstly the gradient of an image is calculated and a threshold is set to obtain a binary gradient image, which is less sensitive to noise and illumination changes. Secondly, significant blocks are extracted from the absolute gradient image, to extract pertinent information with the idea of dimension reduction. Finally the best fitted Hough peaks are extracted from the Hough transformed significant blocks for efficient face recognition. Then these Hough peaks are concatenated together, which are used as feature in classification process. The efficiency of the proposed method is demonstrated by the experiment on 1100 images from the FRAV2D face database, 2200 images from the FERET database, where the images vary in pose, expression, illumination and scale and 400 images from the ORL face database, where the images slightly vary in pose. Our method has shown 93.3%, 88.5% and 99% recognition accuracy for the FRAV2D, FERET and the ORL database respectively.
An adaptive block based integrated LDP,GLCM,and Morphological features for Face Recognition
Authors: Arindam Kar, Debotosh Bhattacharjee, Dipak Kumar Basu, Mita Nasipuri, Mahantapas Kundu
Date: 2013-12-05 00:00:00
Tasks: Dimensionality Reduction, Face Recognition

This paper proposes a technique for automatic face recognition using integrated multiple feature sets extracted from the significant blocks of a gradient image. We discuss about the use of novel morphological, local directional pattern (LDP) and gray-level co-occurrence matrix GLCM based feature extraction technique to recognize human faces. Firstly, the new morphological features i.e., features based on number of runs of pixels in four directions (N,NE,E,NW) are extracted, together with the GLCM based statistical features and LDP features that are less sensitive to the noise and non-monotonic illumination changes, are extracted from the significant blocks of the gradient image. Then these features are concatenated together. We integrate the above mentioned methods to take full advantage of the three approaches. Extraction of the significant blocks from the absolute gradient image and hence from the original image to extract pertinent information with the idea of dimension reduction forms the basis of the work. The efficiency of our method is demonstrated by the experiment on 1100 images from the FRAV2D face database, 2200 images from the FERET database, where the images vary in pose, expression, illumination and scale and 400 images from the ORL face database, where the images slightly vary in pose. Our method has shown 90.3%, 93% and 98.75% recognition accuracy for the FRAV2D, FERET and the ORL database respectively.
Geometric Feature Based Face-Sketch Recognition
Authors: Sourav Pramanik, Debotosh Bhattacharjee
Date: 2013-12-05 00:00:00
Tasks: Sketch Recognition

This paper presents a novel facial sketch image or face-sketch recognition approach based on facial feature extraction. To recognize a face-sketch, we have concentrated on a set of geometric face features like eyes, nose, eyebrows, lips, etc and their length and width ratio because it is difficult to match photos and sketches because they belong to two different modalities. In this system, first the facial features/components from training images are extracted, then ratios of length, width, and area etc. are calculated and those are stored as feature vectors for individual images. After that the mean feature vectors are computed and subtracted from each feature vector for centering of the feature vectors. In the next phase, feature vector for the incoming probe face-sketch is also computed in similar fashion. Here, K-NN classifier is used to recognize probe face-sketch. It is experimentally verified that the proposed method is robust against faces are in a frontal pose, with normal lighting and neutral expression and have no occlusions. The experiment has been conducted with 80 male and female face images from different face databases. It has useful applications for both law enforcement and digital entertainment.
Multi-Sensor Image Fusion Based on Moment Calculation
Authors: Sourav Pramanik, Debotosh Bhattacharjee
Date: 2013-12-05 00:00:00

An image fusion method based on salient features is proposed in this paper.
In this work, we have concentrated on salient features of the image for fusion
in order to preserve all relevant information contained in the input images and
tried to enhance the contrast in fused image and also suppressed noise to a
maximum extent. In our system, first we have applied a mask on two input images
in order to conserve the high frequency information along with some low
frequency information and stifle noise to a maximum extent. Thereafter, for
identification of salience features from sources images, a local moment is
computed in the neighborhood of a coefficient. Finally, a decision map is
generated based on local moment in order to get the fused image. To verify our
proposed algorithm, we have tested it on 120 sensor image pairs collected from
Manchester University UK database. The experimental results show that the
proposed method can provide superior fused image in terms of several
quantitative fusion evaluation index.
An Approach: Modality Reduction and Face-Sketch Recognition
Authors: Sourav Pramanik, Dr. Debotosh Bhattacharjee
Date: 2013-12-05 00:00:00
Tasks: Sketch Recognition

To recognize face sketch through face photo database is a challenging task for todays researchers. Because face photo images in training set and face sketch images in testing set have different modality. Difference between two face photos of difference person is smaller than the difference between same person in a face photo and face sketched. In this paper, for reduction of the modality between face photo and face sketch we first bring face photo and face sketch images in a new dimension using 2D Discrete Haar wavelet transform with scale 3 followed by a negative approach. After that, extract features from transformed images using Principal Component Analysis (PCA). Thereafter, we use SVM classifier and K-NN classifier for better classification. Our proposed method is experimentally verified by its robustness against faces that are captured in a good lighting condition and in a frontal pose. The experiment has been conducted with 100 male and female face images as training set and 100 male and female face sketch images as testing set collected from CUHK training and testing cropped photos and CUHK training and testing cropped sketches.
ABC-SG: A New Artificial Bee Colony Algorithm-Based Distance of Sequential Data Using Sigma Grams
Authors: Muhammad Marwan Muhammad Fuad
Date: 2013-12-05 00:00:00
Tasks: Retrieval, Text Retrieval

The problem of similarity search is one of the main problems in computer
science. This problem has many applications in text-retrieval, web search,
computational biology, bioinformatics and others. Similarity between two data
objects can be depicted using a similarity measure or a distance metric. There
are numerous distance metrics in the literature, some are used for a particular
data type, and others are more general. In this paper we present a new distance
metric for sequential data which is based on the sum of n-grams. The novelty of
our distance is that these n-grams are weighted using artificial bee colony; a
recent optimization algorithm based on the collective intelligence of a swarm
of bees on their search for nectar. This algorithm has been used in optimizing
a large number of numerical problems. We validate the new distance
experimentally.
Chebushev Greedy Algorithm in convex optimization
Authors: Vladimir Temlyakov
Date: 2013-12-04 00:00:00

Chebyshev Greedy Algorithm is a generalization of the well known Orthogonal
Matching Pursuit defined in a Hilbert space to the case of Banach spaces. We
apply this algorithm for constructing sparse approximate solutions (with
respect to a given dictionary) to convex optimization problems. Rate of
convergence results in a style of the Lebesgue-type inequalities are proved.
Case-Based Merging Techniques in OAKPLAN
Authors: Anna Roubíčková, Ivan Serina
Date: 2013-12-04 00:00:00

Case-based planning can take advantage of former problem-solving experiences
by storing in a plan library previously generated plans that can be reused to
solve similar planning problems in the future. Although comparative worst-case
complexity analyses of plan generation and reuse techniques reveal that it is
not possible to achieve provable efficiency gain of reuse over generation, we
show that the case-based planning approach can be an effective alternative to
plan generation when similar reuse candidates can be chosen.
Interpreting random forest classification models using a feature contribution method
Authors: Anna Palczewska, Jan Palczewski, Richard Marchese Robinson, Daniel Neagu
Date: 2013-12-04 00:00:00
Tasks: Classification, General Classification

Model interpretation is one of the key aspects of the model evaluation
process. The explanation of the relationship between model variables and
outputs is relatively easy for statistical models, such as linear regressions,
thanks to the availability of model parameters and their statistical
significance. For "black box" models, such as random forest, this information
is hidden inside the model structure. This work presents an approach for
computing feature contributions for random forest classification models. It
allows for the determination of the influence of each variable on the model
prediction for an individual instance. By analysing feature contributions for a
training dataset, the most significant variables can be determined and their
typical contribution towards predictions made for individual classes, i.e.,
class-specific feature contribution "patterns", are discovered. These patterns
represent a standard behaviour of the model and allow for an additional
assessment of the model reliability for a new data. Interpretation of feature
contributions for two UCI benchmark datasets shows the potential of the
proposed methodology. The robustness of results is demonstrated through an
extensive analysis of feature contributions calculated for a large number of
generated random forest models.
Multiscale Dictionary Learning for Estimating Conditional Distributions
Authors: Francesca Petralia, Joshua Vogelstein, David B. Dunson
Date: 2013-12-04 00:00:00
Proceeding: NeurIPS 2013 12
Tasks: Dictionary Learning, graph partitioning, Tree Decomposition

Nonparametric estimation of the conditional distribution of a response given
high-dimensional features is a challenging problem. It is important to allow
not only the mean but also the variance and shape of the response density to
change flexibly with features, which are massive-dimensional. We propose a
multiscale dictionary learning model, which expresses the conditional response
density as a convex combination of dictionary densities, with the densities
used and their weights dependent on the path through a tree decomposition of
the feature space. A fast graph partitioning algorithm is applied to obtain the
tree decomposition, with Bayesian methods then used to adaptively prune and
average over different sub-trees in a soft probabilistic manner. The algorithm
scales efficiently to approximately one million features. State of the art
predictive performance is demonstrated for toy examples and two neuroscience
applications including up to a million features.
High Throughput Virtual Screening with Data Level Parallelism in Multi-core Processors
Authors: Upul Senanayake, Rahal Prabuddha, Roshan Ragel
Date: 2013-12-04 00:00:00
Tasks: Molecular Docking

Improving the throughput of molecular docking, a computationally intensive
phase of the virtual screening process, is a highly sought area of research
since it has a significant weight in the drug designing process. With such
improvements, the world might find cures for incurable diseases like HIV
disease and Cancer sooner. Our approach presented in this paper is to utilize a
multi-core environment to introduce Data Level Parallelism (DLP) to the
Autodock Vina software, which is a widely used for molecular docking software.
Autodock Vina already exploits Instruction Level Parallelism (ILP) in
multi-core environments and therefore optimized for such environments. However,
with the results we have obtained, it can be clearly seen that our approach has
enhanced the throughput of the already optimized software by more than six
times. This will dramatically reduce the time consumed for the lead
identification phase in drug designing along with the shift in the processor
technology from multi-core to many-core of the current era. Therefore, we
believe that the contribution of this project will effectively make it possible
to expand the number of small molecules docked against a drug target and
improving the chances to design drugs for incurable diseases.
Algorithms and Hardness for Robust Subspace Recovery
Authors: Moritz Hardt, Ankur Moitra
Date: 2012-11-05 00:00:00

We consider a fundamental problem in unsupervised learning called
\emph{subspace recovery}: given a collection of $m$ points in $\mathbb{R}^n$,
if many but not necessarily all of these points are contained in a
$d$-dimensional subspace $T$ can we find it? The points contained in $T$ are
called {\em inliers} and the remaining points are {\em outliers}. This problem
has received considerable attention in computer science and in statistics. Yet
efficient algorithms from computer science are not robust to {\em adversarial}
outliers, and the estimators from robust statistics are hard to compute in high
dimensions.
  Are there algorithms for subspace recovery that are both robust to outliers
and efficient? We give an algorithm that finds $T$ when it contains more than a
$\frac{d}{n}$ fraction of the points. Hence, for say $d = n/2$ this estimator
is both easy to compute and well-behaved when there are a constant fraction of
outliers. We prove that it is Small Set Expansion hard to find $T$ when the
fraction of errors is any larger, thus giving evidence that our estimator is an
{\em optimal} compromise between efficiency and robustness.
  As it turns out, this basic problem has a surprising number of connections to
other areas including small set expansion, matroid theory and functional
analysis that we make use of here.
Distance Correlation Methods for Discovering Associations in Large Astrophysical Databases
Authors: Elizabeth Martinez-Gomez, Mercedes T. Richards, Donald St. P. Richards
Date: 2013-08-19 00:00:00

High-dimensional, large-sample astrophysical databases of galaxy clusters,
such as the Chandra Deep Field South COMBO-17 database, provide measurements on
many variables for thousands of galaxies and a range of redshifts. Current
understanding of galaxy formation and evolution rests sensitively on
relationships between different astrophysical variables; hence an ability to
detect and verify associations or correlations between variables is important
in astrophysical research. In this paper, we apply a recently defined
statistical measure called the distance correlation coefficient which can be
used to identify new associations and correlations between astrophysical
variables. The distance correlation coefficient applies to variables of any
dimension; it can be used to determine smaller sets of variables that provide
equivalent astrophysical information; it is zero only when variables are
independent; and it is capable of detecting nonlinear associations that are
undetectable by the classical Pearson correlation coefficient. Hence, the
distance correlation coefficient provides more information than the Pearson
coefficient. We analyze numerous pairs of variables in the COMBO-17 database
with the distance correlation method and with the maximal information
coefficient. We show that the Pearson coefficient can be estimated with higher
accuracy from the corresponding distance correlation coefficient than from the
maximal information coefficient. For given values of the Pearson coefficient,
the distance correlation method has a greater ability than the maximal
information coefficient to resolve astrophysical data into highly concentrated
V-shapes, which enhances classification and pattern identification. These
results are observed over a range of redshifts beyond the local universe and
for galaxies from elliptical to spiral.