pwc-archive/papers-with-abstracts huggingface dataset:
Tracking via Motion Estimation with Physically Motivated Inter-Region Constraints
Authors: Omar Arif, Ganesh Sundaramoorthi, Byung-Woo Hong, Anthony Yezzi
Date: 2014-02-06 00:00:00
Tasks: Interactive Segmentation, Motion Estimation, Segmentation

In this paper, we propose a method for tracking structures (e.g., ventricles
and myocardium) in cardiac images (e.g., magnetic resonance) by propagating
forward in time a previous estimate of the structures via a new deformation
estimation scheme that is motivated by physical constraints of fluid motion.
The method employs within structure motion estimation (so that differing
motions among different structures are not mixed) while simultaneously
satisfying the physical constraint in fluid motion that at the interface
between a fluid and a medium, the normal component of the fluid's motion must
match the normal component of the motion of the medium. We show how to estimate
the motion according to the previous considerations in a variational framework,
and in particular, show that these conditions lead to PDEs with boundary
conditions at the interface that resemble Robin boundary conditions and induce
coupling between structures. We illustrate the use of this motion estimation
scheme in propagating a segmentation across frames and show that it leads to
more accurate segmentation than traditional motion estimation that does not
make use of physical constraints. Further, the method is naturally suited to
interactive segmentation methods, which are prominently used in practice in
commercial applications for cardiac analysis, where typically a segmentation
from the previous frame is used to predict a segmentation in the next frame. We
show that our propagation scheme reduces the amount of user interaction by
predicting more accurate segmentations than commonly used and recent
interactive commercial techniques.
Near-Optimal Joint Object Matching via Convex Relaxation
Authors: Yuxin Chen, Leonidas J. Guibas, Qi-Xing Huang
Date: 2014-02-06 00:00:00
Tasks: Object

Joint matching over a collection of objects aims at aggregating information
from a large collection of similar instances (e.g. images, graphs, shapes) to
improve maps between pairs of them. Given multiple matches computed between a
few object pairs in isolation, the goal is to recover an entire collection of
maps that are (1) globally consistent, and (2) close to the provided maps ---
and under certain conditions provably the ground-truth maps. Despite recent
advances on this problem, the best-known recovery guarantees are limited to a
small constant barrier --- none of the existing methods find theoretical
support when more than $50\%$ of input correspondences are corrupted. Moreover,
prior approaches focus mostly on fully similar objects, while it is practically
more demanding to match instances that are only partially similar to each
other.
  In this paper, we develop an algorithm to jointly match multiple objects that
exhibit only partial similarities, given a few pairwise matches that are
densely corrupted. Specifically, we propose to recover the ground-truth maps
via a parameter-free convex program called MatchLift, following a spectral
method that pre-estimates the total number of distinct elements to be matched.
Encouragingly, MatchLift exhibits near-optimal error-correction ability, i.e.
in the asymptotic regime it is guaranteed to work even when a dominant fraction
$1-\Theta\left(\frac{\log^{2}n}{\sqrt{n}}\right)$ of the input maps behave like
random outliers. Furthermore, MatchLift succeeds with minimal input complexity,
namely, perfect matching can be achieved as soon as the provided maps form a
connected map graph. We evaluate the proposed algorithm on various benchmark
data sets including synthetic examples and real-world examples, all of which
confirm the practical applicability of MatchLift.
Beating the Minimax Rate of Active Learning with Prior Knowledge
Authors: Lijun Zhang, Mehrdad Mahdavi, Rong Jin
Date: 2013-11-19 00:00:00
Tasks: Active Learning

Active learning refers to the learning protocol where the learner is allowed
to choose a subset of instances for labeling. Previous studies have shown that,
compared with passive learning, active learning is able to reduce the label
complexity exponentially if the data are linearly separable or satisfy the
Tsybakov noise condition with parameter $\kappa=1$. In this paper, we propose a
novel active learning algorithm using a convex surrogate loss, with the goal to
broaden the cases for which active learning achieves an exponential
improvement. We make use of a convex loss not only because it reduces the
computational cost, but more importantly because it leads to a tight bound for
the empirical process (i.e., the difference between the empirical estimation
and the expectation) when the current solution is close to the optimal one.
Under the assumption that the norm of the optimal classifier that minimizes the
convex risk is available, our analysis shows that the introduction of the
convex surrogate loss yields an exponential reduction in the label complexity
even when the parameter $\kappa$ of the Tsybakov noise is larger than $1$. To
the best of our knowledge, this is the first work that improves the minimax
rate of active learning by utilizing certain priori knowledge.
An Autoencoder Approach to Learning Bilingual Word Representations
Authors: Sarath Chandar A P, Stanislas Lauly, Hugo Larochelle, Mitesh M. Khapra, Balaraman Ravindran, Vikas Raykar, Amrita Saha
Date: 2014-02-06 00:00:00
Proceeding: NeurIPS 2014 12

Cross-language learning allows us to use training data from one language to
build models for a different language. Many approaches to bilingual learning
require that we have word-level alignment of sentences from parallel corpora.
In this work we explore the use of autoencoder-based methods for cross-language
learning of vectorial word representations that are aligned between two
languages, while not relying on word-level alignments. We show that by simply
learning to reconstruct the bag-of-words representations of aligned sentences,
within and between languages, we can in fact learn high-quality representations
and do without word alignments. Since training autoencoders on word
observations presents certain computational issues, we propose and compare
different variations adapted to this setting. We also propose an explicit
correlation maximizing regularizer that leads to significant improvement in the
performance. We empirically investigate the success of our approach on the
problem of cross-language test classification, where a classifier trained on a
given language (e.g., English) must learn to generalize to a different language
(e.g., German). These experiments demonstrate that our approaches are
competitive with the state-of-the-art, achieving up to 10-14 percentage point
improvements over the best reported results on this task.
A Three-Phase Search Approach for the Quadratic Minimum Spanning Tree Problem
Authors: Zhang-Hua Fu, Jin-Kao Hao
Date: 2014-02-06 00:00:00

Given an undirected graph with costs associated with each edge as well as
each pair of edges, the quadratic minimum spanning tree problem (QMSTP)
consists of determining a spanning tree of minimum total cost. This problem can
be used to model many real-life network design applications, in which both
routing and interference costs should be considered. For this problem, we
propose a three-phase search approach named TPS, which integrates 1) a
descent-based neighborhood search phase using two different move operators to
reach a local optimum from a given starting solution, 2) a local optima
exploring phase to discover nearby local optima within a given regional search
area, and 3) a perturbation-based diversification phase to jump out of the
current regional search area. Additionally, we introduce dedicated techniques
to reduce the neighborhood to explore and streamline the neighborhood
evaluations. Computational experiments based on hundreds of representative
benchmarks show that TPS produces highly competitive results with respect to
the best performing approaches in the literature by improving the best known
results for 31 instances and matching the best known results for the remaining
instances only except two cases. Critical elements of the proposed algorithms
are analyzed.
Quantile Representation for Indirect Immunofluorescence Image Classification
Authors: David M. J. Tax, Veronika Cheplygina, Marco Loog
Date: 2014-02-06 00:00:00
Tasks: All, Classification, General Classification, image-classification, Image Classification

In the diagnosis of autoimmune diseases, an important task is to classify
images of slides containing several HEp-2 cells. All cells from one slide share
the same label, and by classifying cells from one slide independently, some
information on the global image quality and intensity is lost. Considering one
whole slide as a collection (a bag) of feature vectors, however, poses the
problem of how to handle this bag. A simple, and surprisingly effective,
approach is to summarize the bag of feature vectors by a few quantile values
per feature. This characterizes the full distribution of all instances, thereby
assuming that all instances in a bag are informative. This representation is
particularly useful when each bag contains many feature vectors, which is the
case in the classification of the immunofluorescence images. Experiments on the
classification of indirect immunofluorescence images show the usefulness of
this approach.
Combining finite and continuous solvers
Authors: Jean-Guillaume Fages, Gilles Chabert, Charles Prud'homme
Date: 2014-02-06 00:00:00

Combining efficiency with reliability within CP systems is one of the main
concerns of CP developers. This paper presents a simple and efficient way to
connect Choco and Ibex, two CP solvers respectively specialised on finite and
continuous domains. This enables to take advantage of the most recent advances
of the continuous community within Choco while saving development and
maintenance resources, hence ensuring a better software quality.
Real-time Pedestrian Surveillance with Top View Cumulative Grids
Authors: Kai Berger, Jeyarajan Thiyagalingam
Date: 2014-02-06 00:00:00
Tasks: Object Tracking

This manuscript presents an efficient approach to map pedestrian surveillance
footage to an aerial view for global assessment of features. The analysis of
the footages relies on low level computer vision and enable real-time
surveillance. While we neglect object tracking, we introduce cumulative grids
on top view scene flow visualization to highlight situations of interest in the
footage. Our approach is tested on multiview footage both from RGB cameras and,
for the first time in the field, on RGB-D-sensors.
Dissimilarity-based Ensembles for Multiple Instance Learning
Authors: Veronika Cheplygina, David M. J. Tax, Marco Loog
Date: 2014-02-06 00:00:00
Tasks: Multiple Instance Learning

In multiple instance learning, objects are sets (bags) of feature vectors
(instances) rather than individual feature vectors. In this paper we address
the problem of how these bags can best be represented. Two standard approaches
are to use (dis)similarities between bags and prototype bags, or between bags
and prototype instances. The first approach results in a relatively
low-dimensional representation determined by the number of training bags, while
the second approach results in a relatively high-dimensional representation,
determined by the total number of instances in the training set. In this paper
a third, intermediate approach is proposed, which links the two approaches and
combines their strengths. Our classifier is inspired by a random subspace
ensemble, and considers subspaces of the dissimilarity space, defined by
subsets of instances, as prototypes. We provide guidelines for using such an
ensemble, and show state-of-the-art performances on a range of multiple
instance learning problems.
A Cellular Automata based Optimal Edge Detection Technique using Twenty-Five Neighborhood Model
Authors: Deepak Ranjan Nayak, Sumit Kumar Sahu, Jahangir Mohammed
Date: 2014-02-06 00:00:00
Tasks: Edge Detection

Cellular Automata (CA) are common and most simple models of parallel
computations. Edge detection is one of the crucial task in image processing,
especially in processing biological and medical images. CA can be successfully
applied in image processing. This paper presents a new method for edge
detection of binary images based on two dimensional twenty five neighborhood
cellular automata. The method considers only linear rules of CA for extraction
of edges under null boundary condition. The performance of this approach is
compared with some existing edge detection techniques. This comparison shows
that the proposed method to be very promising for edge detection of binary
images. All the algorithms and results used in this paper are prepared in
MATLAB.
Learning Transformations for Classification Forests
Authors: Qiang Qiu, Guillermo Sapiro
Date: 2013-12-19 00:00:00
Tasks: Classification, General Classification

This work introduces a transformation-based learner model for classification
forests. The weak learner at each split node plays a crucial role in a
classification tree. We propose to optimize the splitting objective by learning
a linear transformation on subspaces using nuclear norm as the optimization
criteria. The learned linear transformation restores a low-rank structure for
data from the same class, and, at the same time, maximizes the separation
between different classes, thereby improving the performance of the split
function. Theoretical and experimental results support the proposed framework.
An Estimation Method of Measuring Image Quality for Compressed Images of Human Face
Authors: Abhishek Bhattacharya, Tanusree Chatterjee
Date: 2014-02-06 00:00:00
Tasks: Image Compression, Image Segmentation, Segmentation, Semantic Segmentation, SSIM

Nowadays digital image compression and decompression techniques are very much important. So our aim is to calculate the quality of face and other regions of the compressed image with respect to the original image. Image segmentation is typically used to locate objects and boundaries (lines, curves etc.)in images. After segmentation the image is changed into something which is more meaningful to analyze. Using Universal Image Quality Index(Q),Structural Similarity Index(SSIM) and Gradient-based Structural Similarity Index(G-SSIM) it can be shown that face region is less compressed than any other region of the image.
A density-sensitive hierarchical clustering method
Authors: Álvaro Martínez-Pérez
Date: 2012-10-23 00:00:00
Tasks: Clustering

We define a hierarchical clustering method: $\alpha$-unchaining single
linkage or $SL(\alpha)$. The input of this algorithm is a finite metric space
and a certain parameter $\alpha$. This method is sensitive to the density of
the distribution and offers some solution to the so called chaining effect. We
also define a modified version, $SL^*(\alpha)$, to treat the chaining through
points or small blocks. We study the theoretical properties of these methods
and offer some theoretical background for the treatment of chaining effects.
Localized epidemic detection in networks with overwhelming noise
Authors: Eli A. Meirom, Chris Milling, Constantine Caramanis, Shie Mannor, Ariel Orda, Sanjay Shakkottai
Date: 2014-02-06 00:00:00

We consider the problem of detecting an epidemic in a population where
individual diagnoses are extremely noisy. The motivation for this problem is
the plethora of examples (influenza strains in humans, or computer viruses in
smartphones, etc.) where reliable diagnoses are scarce, but noisy data
plentiful. In flu/phone-viruses, exceedingly few infected people/phones are
professionally diagnosed (only a small fraction go to a doctor) but less
reliable secondary signatures (e.g., people staying home, or
greater-than-typical upload activity) are more readily available. These
secondary data are often plagued by unreliability: many people with the flu do
not stay home, and many people that stay home do not have the flu. This paper
identifies the precise regime where knowledge of the contact network enables
finding the needle in the haystack: we provide a distributed, efficient and
robust algorithm that can correctly identify the existence of a spreading
epidemic from highly unreliable local data. Our algorithm requires only
local-neighbor knowledge of this graph, and in a broad array of settings that
we describe, succeeds even when false negatives and false positives make up an
overwhelming fraction of the data available. Our results show it succeeds in
the presence of partial information about the contact network, and also when
there is not a single "patient zero", but rather many (hundreds, in our
examples) of initial patient-zeroes, spread across the graph.
Multispectral Palmprint Encoding and Recognition
Authors: Zohaib Khan, Faisal Shafait, Yiqun Hu, Ajmal Mian
Date: 2014-02-06 00:00:00

Palmprints are emerging as a new entity in multi-modal biometrics for human
identification and verification. Multispectral palmprint images captured in the
visible and infrared spectrum not only contain the wrinkles and ridge structure
of a palm, but also the underlying pattern of veins; making them a highly
discriminating biometric identifier. In this paper, we propose a feature
encoding scheme for robust and highly accurate representation and matching of
multispectral palmprints. To facilitate compact storage of the feature, we
design a binary hash table structure that allows for efficient matching in
large databases. Comprehensive experiments for both identification and
verification scenarios are performed on two public datasets -- one captured
with a contact-based sensor (PolyU dataset), and the other with a contact-free
sensor (CASIA dataset). Recognition results in various experimental setups show
that the proposed method consistently outperforms existing state-of-the-art
methods. Error rates achieved by our method (0.003% on PolyU and 0.2% on CASIA)
are the lowest reported in literature on both dataset and clearly indicate the
viability of palmprint as a reliable and promising biometric. All source codes
are publicly available.
Demystifying Information-Theoretic Clustering
Authors: Greg Ver Steeg, Aram Galstyan, Fei Sha, Simon DeDeo
Date: 2013-10-15 00:00:00
Tasks: Clustering

We propose a novel method for clustering data which is grounded in
information-theoretic principles and requires no parametric assumptions.
Previous attempts to use information theory to define clusters in an
assumption-free way are based on maximizing mutual information between data and
cluster labels. We demonstrate that this intuition suffers from a fundamental
conceptual flaw that causes clustering performance to deteriorate as the amount
of data increases. Instead, we return to the axiomatic foundations of
information theory to define a meaningful clustering measure based on the
notion of consistency under coarse-graining for finite data.
Image Acquisition in an Underwater Vision System with NIR and VIS Illumination
Authors: Wojciech Biegański, Andrzej Kasiński
Date: 2014-02-05 00:00:00
Tasks: Autonomous Navigation

The paper describes the image acquisition system able to capture images in
two separated bands of light, used to underwater autonomous navigation. The
channels are: the visible light spectrum and near infrared spectrum. The
characteristics of natural, underwater environment were also described together
with the process of the underwater image creation. The results of an experiment
with comparison of selected images acquired in these channels are discussed.
An enhanced neural network based approach towards object extraction
Authors: S. K. Katiyar, P. V. Arun
Date: 2014-02-05 00:00:00
Tasks: Object

The improvements in spectral and spatial resolution of the satellite images
have facilitated the automatic extraction and identification of the features
from satellite images and aerial photographs. An automatic object extraction
method is presented for extracting and identifying the various objects from
satellite images and the accuracy of the system is verified with regard to IRS
satellite images. The system is based on neural network and simulates the
process of visual interpretation from remote sensing images and hence increases
the efficiency of image analysis. This approach obtains the basic
characteristics of the various features and the performance is enhanced by the
automatic learning approach, intelligent interpretation, and intelligent
interpolation. The major advantage of the method is its simplicity and that the
system identifies the features not only based on pixel value but also based on
the shape, haralick features etc of the objects. Further the system allows
flexibility for identifying the features within the same category based on size
and shape. The successful application of the system verified its effectiveness
and the accuracy of the system were assessed by ground truth verification.
Quantum Cybernetics and Complex Quantum Systems Science - A Quantum Connectionist Exploration
Authors: Carlos Pedro Gonçalves
Date: 2014-02-05 00:00:00

Quantum cybernetics and its connections to complex quantum systems science is
addressed from the perspective of complex quantum computing systems. In this
way, the notion of an autonomous quantum computing system is introduced in
regards to quantum artificial intelligence, and applied to quantum artificial
neural networks, considered as autonomous quantum computing systems, which
leads to a quantum connectionist framework within quantum cybernetics for
complex quantum computing systems. Several examples of quantum feedforward
neural networks are addressed in regards to Boolean functions' computation,
multilayer quantum computation dynamics, entanglement and quantum
complementarity. The examples provide a framework for a reflection on the role
of quantum artificial neural networks as a general framework for addressing
complex quantum systems that perform network-based quantum computation,
possible consequences are drawn regarding quantum technologies, as well as
fundamental research in complex quantum systems science and quantum biology.
Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognition
Authors: Haşim Sak, Andrew Senior, Françoise Beaufays
Date: 2014-02-05 00:00:00
Tasks: Handwriting Recognition, Language Modeling, Language Modelling, speech-recognition, Speech Recognition

Long Short-Term Memory (LSTM) is a recurrent neural network (RNN)
architecture that has been designed to address the vanishing and exploding
gradient problems of conventional RNNs. Unlike feedforward neural networks,
RNNs have cyclic connections making them powerful for modeling sequences. They
have been successfully used for sequence labeling and sequence prediction
tasks, such as handwriting recognition, language modeling, phonetic labeling of
acoustic frames. However, in contrast to the deep neural networks, the use of
RNNs in speech recognition has been limited to phone recognition in small scale
tasks. In this paper, we present novel LSTM based RNN architectures which make
more effective use of model parameters to train acoustic models for large
vocabulary speech recognition. We train and compare LSTM, RNN and DNN models at
various numbers of parameters and configurations. We show that LSTM models
converge quickly and give state of the art speech recognition performance for
relatively small sized models.
Linear and Parallel Learning of Markov Random Fields
Authors: Yariv Dror Mizrahi, Misha Denil, Nando de Freitas
Date: 2013-08-29 00:00:00

We introduce a new embarrassingly parallel parameter learning algorithm for
Markov random fields with untied parameters which is efficient for a large
class of practical models. Our algorithm parallelizes naturally over cliques
and, for graphs of bounded degree, its complexity is linear in the number of
cliques. Unlike its competitors, our algorithm is fully parallel and for
log-linear models it is also data efficient, requiring only the local
sufficient statistics of the data to estimate parameters.