pwc-archive/papers-with-abstracts huggingface dataset:
A Primal Dual Active Set with Continuation Algorithm for the \ell^0-Regularized Optimization Problem
Authors: Yuling Jiao, Bangti Jin, Xiliang Lu
Date: 2014-03-03 00:00:00
Tasks: compressed sensing

We develop a primal dual active set with continuation algorithm for solving
the \ell^0-regularized least-squares problem that frequently arises in
compressed sensing. The algorithm couples the the primal dual active set method
with a continuation strategy on the regularization parameter. At each inner
iteration, it first identifies the active set from both primal and dual
variables, and then updates the primal variable by solving a (typically small)
least-squares problem defined on the active set, from which the dual variable
can be updated explicitly. Under certain conditions on the sensing matrix,
i.e., mutual incoherence property or restricted isometry property, and the
noise level, the finite step global convergence of the algorithm is
established. Extensive numerical examples are presented to illustrate the
efficiency and accuracy of the algorithm and the convergence analysis.
Face Recognition Methods & Applications
Authors: Divyarajsinh N. Parmar, Brijesh B. Mehta
Date: 2014-03-03 00:00:00
Tasks: Face Recognition

Face recognition presents a challenging problem in the field of image analysis and computer vision. The security of information is becoming very significant and difficult. Security cameras are presently common in airports, Offices, University, ATM, Bank and in any locations with a security system. Face recognition is a biometric system used to identify or verify a person from a digital image. Face Recognition system is used in security. Face recognition system should be able to automatically detect a face in an image. This involves extracts its features and then recognize it, regardless of lighting, expression, illumination, ageing, transformations (translate, rotate and scale image) and pose, which is a difficult task. This paper contains three sections. The first section describes the common methods like holistic matching method, feature extraction method and hybrid methods. The second section describes applications with examples and finally third section describes the future research directions of face recognition.
Support Vector Machine Model for Currency Crisis Discrimination
Authors: Arindam Chaudhuri
Date: 2014-03-03 00:00:00

Support Vector Machine (SVM) is powerful classification technique based on
the idea of structural risk minimization. Use of kernel function enables curse
of dimensionality to be addressed. However, proper kernel function for certain
problem is dependent on specific dataset and as such there is no good method on
choice of kernel function. In this paper, SVM is used to build empirical models
of currency crisis in Argentina. An estimation technique is developed by
training model on real life data set which provides reasonably accurate model
outputs and helps policy makers to identify situations in which currency crisis
may happen. The third and fourth order polynomial kernel is generally best
choice to achieve high generalization of classifier performance. SVM has high
level of maturity with algorithms that are simple, easy to implement, tolerates
curse of dimensionality and good empirical performance. The satisfactory
results show that currency crisis situation is properly emulated using only
small fraction of database and could be used as an evaluation tool as well as
an early warning system. To the best of knowledge this is the first work on SVM
approach for currency crisis evaluation of Argentina.
Matching Image Sets via Adaptive Multi Convex Hull
Authors: Shaokang Chen, Arnold Wiliem, Conrad Sanderson, Brian C. Lovell
Date: 2014-03-03 00:00:00
Tasks: ARC, Clustering, General Classification

Traditional nearest points methods use all the samples in an image set to
construct a single convex or affine hull model for classification. However,
strong artificial features and noisy data may be generated from combinations of
training samples when significant intra-class variations and/or noise occur in
the image set. Existing multi-model approaches extract local models by
clustering each image set individually only once, with fixed clusters used for
matching with various image sets. This may not be optimal for discrimination,
as undesirable environmental conditions (eg. illumination and pose variations)
may result in the two closest clusters representing different characteristics
of an object (eg. frontal face being compared to non-frontal face). To address
the above problem, we propose a novel approach to enhance nearest points based
methods by integrating affine/convex hull classification with an adapted
multi-model approach. We first extract multiple local convex hulls from a query
image set via maximum margin clustering to diminish the artificial variations
and constrain the noise in local convex hulls. We then propose adaptive
reference clustering (ARC) to constrain the clustering of each gallery image
set by forcing the clusters to have resemblance to the clusters in the query
image set. By applying ARC, noisy clusters in the query set can be discarded.
Experiments on Honda, MoBo and ETH-80 datasets show that the proposed method
outperforms single model approaches and other recent techniques, such as Sparse
Approximated Nearest Points, Mutual Subspace Method and Manifold Discriminant
Analysis.
Cross-Scale Cost Aggregation for Stereo Matching
Authors: Kang Zhang, Yuqiang Fang, Dongbo Min, Lifeng Sun, Shiqiang Yang. Shuicheng Yan, Qi Tian
Date: 2014-03-03 00:00:00
Proceeding: CVPR 2014 6
Tasks: Stereo Matching, Stereo Matching Hand

Human beings process stereoscopic correspondence across multiple scales.
However, this bio-inspiration is ignored by state-of-the-art cost aggregation
methods for dense stereo correspondence. In this paper, a generic cross-scale
cost aggregation framework is proposed to allow multi-scale interaction in cost
aggregation. We firstly reformulate cost aggregation from a unified
optimization perspective and show that different cost aggregation methods
essentially differ in the choices of similarity kernels. Then, an inter-scale
regularizer is introduced into optimization and solving this new optimization
problem leads to the proposed framework. Since the regularization term is
independent of the similarity kernel, various cost aggregation methods can be
integrated into the proposed general framework. We show that the cross-scale
framework is important as it effectively and efficiently expands
state-of-the-art cost aggregation methods and leads to significant
improvements, when evaluated on Middlebury, KITTI and New Tsukuba datasets.
Summarisation of Short-Term and Long-Term Videos using Texture and Colour
Authors: Johanna Carvajal, Chris McCool, Conrad Sanderson
Date: 2014-03-03 00:00:00

We present a novel approach to video summarisation that makes use of a
Bag-of-visual-Textures (BoT) approach. Two systems are proposed, one based
solely on the BoT approach and another which exploits both colour information
and BoT features. On 50 short-term videos from the Open Video Project we show
that our BoT and fusion systems both achieve state-of-the-art performance,
obtaining an average F-measure of 0.83 and 0.86 respectively, a relative
improvement of 9% and 13% when compared to the previous state-of-the-art. When
applied to a new underwater surveillance dataset containing 33 long-term
videos, the proposed system reduces the amount of footage by a factor of 27,
with only minor degradation in the information content. This order of magnitude
reduction in video data represents significant savings in terms of time and
potential labour cost when manually reviewing such footage.
Object Tracking via Non-Euclidean Geometry: A Grassmann Approach
Authors: Sareh Shirazi, Mehrtash T. Harandi, Brian C. Lovell, Conrad Sanderson
Date: 2014-03-03 00:00:00
Tasks: Object, Object Tracking, Visual Tracking

A robust visual tracking system requires an object appearance model that is
able to handle occlusion, pose, and illumination variations in the video
stream. This can be difficult to accomplish when the model is trained using
only a single image. In this paper, we first propose a tracking approach based
on affine subspaces (constructed from several images) which are able to
accommodate the abovementioned variations. We use affine subspaces not only to
represent the object, but also the candidate areas that the object may occupy.
We furthermore propose a novel approach to measure affine subspace-to-subspace
distance via the use of non-Euclidean geometry of Grassmann manifolds. The
tracking problem is then considered as an inference task in a Markov Chain
Monte Carlo framework via particle filtering. Quantitative evaluation on
challenging video sequences indicates that the proposed approach obtains
considerably better performance than several recent state-of-the-art methods
such as Tracking-Learning-Detection and MILtrack.
Blind and fully constrained unmixing of hyperspectral images
Authors: Rita Ammanouil, André Ferrari, Cédric Richard, David Mary
Date: 2014-03-03 00:00:00

This paper addresses the problem of blind and fully constrained unmixing of
hyperspectral images. Unmixing is performed without the use of any dictionary,
and assumes that the number of constituent materials in the scene and their
spectral signatures are unknown. The estimated abundances satisfy the desired
sum-to-one and nonnegativity constraints. Two models with increasing complexity
are developed to achieve this challenging task, depending on how noise
interacts with hyperspectral data. The first one leads to a convex optimization
problem, and is solved with the Alternating Direction Method of Multipliers.
The second one accounts for signal-dependent noise, and is addressed with a
Reweighted Least Squares algorithm. Experiments on synthetic and real data
demonstrate the effectiveness of our approach.
Multiview Hessian regularized logistic regression for action recognition
Authors: W. Liu, H. Liu, D. Tao, Y. Wang, Ke Lu
Date: 2014-03-03 00:00:00
Tasks: Action Recognition, Attribute, General Classification, regression, Temporal Action Localization, Video Classification

With the rapid development of social media sharing, people often need to
manage the growing volume of multimedia data such as large scale video
classification and annotation, especially to organize those videos containing
human activities. Recently, manifold regularized semi-supervised learning
(SSL), which explores the intrinsic data probability distribution and then
improves the generalization ability with only a small number of labeled data,
has emerged as a promising paradigm for semiautomatic video classification. In
addition, human action videos often have multi-modal content and different
representations. To tackle the above problems, in this paper we propose
multiview Hessian regularized logistic regression (mHLR) for human action
recognition. Compared with existing work, the advantages of mHLR lie in three
folds: (1) mHLR combines multiple Hessian regularization, each of which
obtained from a particular representation of instance, to leverage the
exploring of local geometry; (2) mHLR naturally handle multi-view instances
with multiple representations; (3) mHLR employs a smooth loss function and then
can be effectively optimized. We carefully conduct extensive experiments on the
unstructured social activity attribute (USAA) dataset and the experimental
results demonstrate the effectiveness of the proposed multiview Hessian
regularized logistic regression for human action recognition.
Particle methods enable fast and simple approximation of Sobolev gradients in image segmentation
Authors: Ivo F. Sbalzarini, Sophie Schneider, Janick Cardinale
Date: 2014-03-02 00:00:00
Tasks: Bayesian Inference, Image Segmentation, Segmentation, Semantic Segmentation

Bio-image analysis is challenging due to inhomogeneous intensity
distributions and high levels of noise in the images. Bayesian inference
provides a principled way for regularizing the problem using prior knowledge. A
fundamental choice is how one measures "distances" between shapes in an image.
It has been shown that the straightforward geometric L2 distance is degenerate
and leads to pathological situations. This is avoided when using Sobolev
gradients, rendering the segmentation problem less ill-posed. The high
computational cost and implementation overhead of Sobolev gradients, however,
have hampered practical applications. We show how particle methods as applied
to image segmentation allow for a simple and computationally efficient
implementation of Sobolev gradients. We show that the evaluation of Sobolev
gradients amounts to particle-particle interactions along the contour in an
image. We extend an existing particle-based segmentation algorithm to using
Sobolev gradients. Using synthetic and real-world images, we benchmark the
results for both 2D and 3D images using piecewise smooth and piecewise constant
region models. The present particle approximation of Sobolev gradients is 2.8
to 10 times faster than the previous reference implementation, but retains the
known favorable properties of Sobolev gradients. This speedup is achieved by
using local particle-particle interactions instead of solving a global Poisson
equation at each iteration. The computational time per iteration is higher for
Sobolev gradients than for L2 gradients. Since Sobolev gradients precondition
the optimization problem, however, a smaller number of overall iterations may
be necessary for the algorithm to converge, which can in some cases amortize
the higher per-iteration cost.
Learning Using Privileged Information: SVM+ and Weighted SVM
Authors: Maksim Lapin, Matthias Hein, Bernt Schiele
Date: 2013-06-13 00:00:00

Prior knowledge can be used to improve predictive performance of learning
algorithms or reduce the amount of data required for training. The same goal is
pursued within the learning using privileged information paradigm which was
recently introduced by Vapnik et al. and is aimed at utilizing additional
information available only at training time -- a framework implemented by SVM+.
We relate the privileged information to importance weighting and show that the
prior knowledge expressible with privileged features can also be encoded by
weights associated with every training example. We show that a weighted SVM can
always replicate an SVM+ solution, while the converse is not true and we
construct a counterexample highlighting the limitations of SVM+. Finally, we
touch on the problem of choosing weights for weighted SVMs when privileged
features are not available.
Network Traffic Decomposition for Anomaly Detection
Authors: Tahereh Babaie, Sanjay Chawla, Sebastien Ardon
Date: 2014-03-02 00:00:00
Tasks: Anomaly Detection

In this paper we focus on the detection of network anomalies like Denial of
Service (DoS) attacks and port scans in a unified manner. While there has been
an extensive amount of research in network anomaly detection, current state of
the art methods are only able to detect one class of anomalies at the cost of
others. The key tool we will use is based on the spectral decomposition of a
trajectory/hankel matrix which is able to detect deviations from both between
and within correlation present in the observed network traffic data. Detailed
experiments on synthetic and real network traces shows a significant
improvement in detection capability over competing approaches. In the process
we also address the issue of robustness of anomaly detection systems in a
principled fashion.
Sleep Analytics and Online Selective Anomaly Detection
Authors: Tahereh Babaie, Sanjay Chawla, Romesh Abeysuriya
Date: 2014-03-02 00:00:00
Tasks: Anomaly Detection, EEG, Electroencephalogram (EEG), Time Series, Time Series Analysis

We introduce a new problem, the Online Selective Anomaly Detection (OSAD), to
model a specific scenario emerging from research in sleep science. Scientists
have segmented sleep into several stages and stage two is characterized by two
patterns (or anomalies) in the EEG time series recorded on sleep subjects.
These two patterns are sleep spindle (SS) and K-complex. The OSAD problem was
introduced to design a residual system, where all anomalies (known and unknown)
are detected but the system only triggers an alarm when non-SS anomalies
appear. The solution of the OSAD problem required us to combine techniques from
both machine learning and control theory. Experiments on data from real
subjects attest to the effectiveness of our approach.
Accelerated, Parallel and Proximal Coordinate Descent
Authors: Olivier Fercoq, Peter Richtárik
Date: 2013-12-20 00:00:00

We propose a new stochastic coordinate descent method for minimizing the sum
of convex functions each of which depends on a small number of coordinates
only. Our method (APPROX) is simultaneously Accelerated, Parallel and PROXimal;
this is the first time such a method is proposed. In the special case when the
number of processors is equal to the number of coordinates, the method
converges at the rate $2\bar{\omega}\bar{L} R^2/(k+1)^2 $, where $k$ is the
iteration counter, $\bar{\omega}$ is an average degree of separability of the
loss function, $\bar{L}$ is the average of Lipschitz constants associated with
the coordinates and individual functions in the sum, and $R$ is the distance of
the initial point from the minimizer. We show that the method can be
implemented without the need to perform full-dimensional vector operations,
which is the major bottleneck of existing accelerated coordinate descent
methods. The fact that the method depends on the average degree of
separability, and not on the maximum degree of separability, can be attributed
to the use of new safe large stepsizes, leading to improved expected separable
overapproximation (ESO). These are of independent interest and can be utilized
in all existing parallel stochastic coordinate descent algorithms based on the
concept of ESO.
Temporal Image Fusion
Authors: Francisco J. Estrada
Date: 2014-03-01 00:00:00

This paper introduces temporal image fusion. The proposed technique builds
upon previous research in exposure fusion and expands it to deal with the
limited Temporal Dynamic Range of existing sensors and camera technologies. In
particular, temporal image fusion enables the rendering of long-exposure
effects on full frame-rate video, as well as the generation of arbitrarily long
exposures from a sequence of images of the same scene taken over time. We
explore the problem of temporal under-exposure, and show how it can be
addressed by selectively enhancing dynamic structure. Finally, we show that the
use of temporal image fusion together with content-selective image filters can
produce a range of striking visual effects on a given input sequence.
TBX goes TEI -- Implementing a TBX basic extension for the Text Encoding Initiative guidelines
Authors: Laurent Romary
Date: 2014-03-01 00:00:00

This paper presents an attempt to customise the TEI (Text Encoding
Initiative) guidelines in order to offer the possibility to incorporate TBX
(TermBase eXchange) based terminological entries within any kind of TEI
documents. After presenting the general historical, conceptual and technical
contexts, we describe the various design choices we had to take while creating
this customisation, which in turn have led to make various changes in the
actual TBX serialisation. Keeping in mind the objective to provide the TEI
guidelines with, again, an onomasiological model, we try to identify the best
comprise in maintaining both the isomorphism with the existing TBX Basic
standard and the characteristics of the TEI framework.
Dynamic Decision Process Modeling and Relation-line Handling in Distributed Cooperative Modeling System
Authors: Menghan Wang
Date: 2014-03-01 00:00:00
Tasks: Decision Making, Relation

The Distributed Cooperative Modeling System (DCMS) solves complex decision
problems involving a lot of participants with different viewpoints by network
based distributed modeling and multi-template aggregation.
  This thesis aims at extending the system with support for dynamic decision
making process. First, the thesis presents a discussion of characteristics and
optimal policy finding Markov Decision Process as well as a brief introduction
to dynamic Bayesian decision network, which is inherently equal to MDP. After
that, discussion and implementation of prediction in Markov process for both
discrete and continuous random variable are given, as well as several different
kinds of correlation analysis among multiple indices which could help
decision-makers to realize the interaction of indices and design appropriate
policy.
  Appending history data of Macau industry, as the foundation of extending
DCMS, is introduced. Additional works include rearrangement of graphical class
hierarchy in DCMS, which in turn allows convenient implementation of curve
relation-line, which makes template modeling clearer and friendlier.
A Machine Learning Model for Stock Market Prediction
Authors: Osman Hegazy, Omar S. Soliman, Mustafa Abdul Salam
Date: 2014-02-28 00:00:00
Tasks: BIG-bench Machine Learning, model, Prediction, Stock Market Prediction

Stock market prediction is the act of trying to determine the future value of
a company stock or other financial instrument traded on a financial exchange.
Robot Location Estimation in the Situation Calculus
Authors: Vaishak Belle, Hector Levesque
Date: 2014-02-28 00:00:00

Location estimation is a fundamental sensing task in robotic applications,
where the world is uncertain, and sensors and effectors are noisy. Most systems
make various assumptions about the dependencies between state variables, and
especially about how these dependencies change as a result of actions. Building
on a general framework by Bacchus, Halpern and Levesque for reasoning about
degrees of belief in the situation calculus, and a recent extension to it for
continuous domains, in this paper we illustrate location estimation in the
presence of a rich theory of actions using an example. We also show that while
actions might affect prior distributions in nonstandard ways, suitable
posterior beliefs are nonetheless entailed as a side-effect of the overall
specification.
Semantics, Modelling, and the Problem of Representation of Meaning -- a Brief Survey of Recent Literature
Authors: Yarin Gal
Date: 2014-02-28 00:00:00
Tasks: Survey

Over the past 50 years many have debated what representation should be used
to capture the meaning of natural language utterances. Recently new needs of
such representations have been raised in research. Here I survey some of the
interesting representations suggested to answer for these new needs.
Visual Saliency Model using SIFT and Comparison of Learning Approaches
Authors: Hamdi Yalin Yalic
Date: 2014-02-28 00:00:00

Humans' ability to detect and locate salient objects on images is remarkably
fast and successful. Performing this process by using eye tracking equipment is
expensive and cannot be easily applied, and computer modeling of this human
behavior is still a problem to be solved. In our study, one of the largest
public eye-tracking databases which has fixation points of 15 observers on 1003
images is used. In addition to low, medium and high-level features which have
been used in previous studies, SIFT features extracted from the images are used
to improve the classification accuracy of the models. A second contribution of
this paper is the comparison and statistical analysis of different machine
learning methods that can be used to train our model. As a result, a best
feature set and learning model to predict where humans look at images, is
determined.