pwc-archive/papers-with-abstracts huggingface dataset:
Implicit segmentation of Kannada characters in offline handwriting recognition using hidden Markov models
Authors: Manasij Venkatesh, Vikas Majjagi, Deepu Vijayasenan
Date: 2014-10-16 00:00:00
Tasks: General Classification, Handwriting Recognition, Segmentation

We describe a method for classification of handwritten Kannada characters
using Hidden Markov Models (HMMs). Kannada script is agglutinative, where
simple shapes are concatenated horizontally to form a character. This results
in a large number of characters making the task of classification difficult.
Character segmentation plays a significant role in reducing the number of
classes. Explicit segmentation techniques suffer when overlapping shapes are
present, which is common in the case of handwritten text. We use HMMs to take
advantage of the agglutinative nature of Kannada script, which allows us to
perform implicit segmentation of characters along with recognition. All the
experiments are performed on the Chars74k dataset that consists of 657
handwritten characters collected across multiple users. Gradient-based features
are extracted from individual characters and are used to train character HMMs.
The use of implicit segmentation technique at the character level resulted in
an improvement of around 10%. This system also outperformed an existing system
tested on the same dataset by around 16%. Analysis based on learning curves
showed that increasing the training data could result in better accuracy.
Accordingly, we collected additional data and obtained an improvement of 4%
with 6 additional samples.
Super-resolution method using sparse regularization for point-spread function recovery
Authors: Fred Maurice Ngolè Mboula, Jean-Luc Starck, Samuel Ronayette, Koryo Okumura, Jérôme Amiaux
Date: 2014-10-16 00:00:00
Tasks: Super-Resolution

In large-scale spatial surveys, such as the forthcoming ESA Euclid mission,
images may be undersampled due to the optical sensors sizes. Therefore, one may
consider using a super-resolution (SR) method to recover aliased frequencies,
prior to further analysis. This is particularly relevant for point-source
images, which provide direct measurements of the instrument point-spread
function (PSF). We introduce SPRITE, SParse Recovery of InsTrumental rEsponse,
which is an SR algorithm using a sparse analysis prior. We show that such a
prior provides significant improvements over existing methods, especially on
low SNR PSFs.
Lasso Screening Rules via Dual Polytope Projection
Authors: Jie Wang, Peter Wonka, Jieping Ye
Date: 2012-11-16 00:00:00
Proceeding: NeurIPS 2013 12

Lasso is a widely used regression technique to find sparse representations.
When the dimension of the feature space and the number of samples are extremely
large, solving the Lasso problem remains challenging. To improve the efficiency
of solving large-scale Lasso problems, El Ghaoui and his colleagues have
proposed the SAFE rules which are able to quickly identify the inactive
predictors, i.e., predictors that have $0$ components in the solution vector.
Then, the inactive predictors or features can be removed from the optimization
problem to reduce its scale. By transforming the standard Lasso to its dual
form, it can be shown that the inactive predictors include the set of inactive
constraints on the optimal dual solution. In this paper, we propose an
efficient and effective screening rule via Dual Polytope Projections (DPP),
which is mainly based on the uniqueness and nonexpansiveness of the optimal
dual solution due to the fact that the feasible set in the dual space is a
convex and closed polytope. Moreover, we show that our screening rule can be
extended to identify inactive groups in group Lasso. To the best of our
knowledge, there is currently no "exact" screening rule for group Lasso. We
have evaluated our screening rule using synthetic and real data sets. Results
show that our rule is more effective in identifying inactive predictors than
existing state-of-the-art screening rules for Lasso.
Two-Layer Feature Reduction for Sparse-Group Lasso via Decomposition of Convex Sets
Authors: Jie Wang, Jieping Ye
Date: 2014-10-15 00:00:00
Proceeding: NeurIPS 2014 12

Sparse-Group Lasso (SGL) has been shown to be a powerful regression technique
for simultaneously discovering group and within-group sparse patterns by using
a combination of the $\ell_1$ and $\ell_2$ norms. However, in large-scale
applications, the complexity of the regularizers entails great computational
challenges. In this paper, we propose a novel Two-Layer Feature REduction
method (TLFre) for SGL via a decomposition of its dual feasible set. The
two-layer reduction is able to quickly identify the inactive groups and the
inactive features, respectively, which are guaranteed to be absent from the
sparse representation and can be removed from the optimization. Existing
feature reduction methods are only applicable for sparse models with one
sparsity-inducing regularizer. To our best knowledge, TLFre is the first one
that is capable of dealing with multiple sparsity-inducing regularizers.
Moreover, TLFre has a very low computational cost and can be integrated with
any existing solvers. We also develop a screening method---called DPC
(DecomPosition of Convex set)---for the nonnegative Lasso problem. Experiments
on both synthetic and real data sets show that TLFre and DPC improve the
efficiency of SGL and nonnegative Lasso by several orders of magnitude.
Learning Distributed Word Representations for Natural Logic Reasoning
Authors: Samuel R. Bowman, Christopher Potts, Christopher D. Manning
Date: 2014-10-15 00:00:00
Tasks: Logical Reasoning, Open-Ended Question Answering, Tensor Networks

Natural logic offers a powerful relational conception of meaning that is a
natural counterpart to distributed semantic representations, which have proven
valuable in a wide range of sophisticated language tasks. However, it remains
an open question whether it is possible to train distributed representations to
support the rich, diverse logical reasoning captured by natural logic. We
address this question using two neural network-based models for learning
embeddings: plain neural networks and neural tensor networks. Our experiments
evaluate the models' ability to learn the basic algebra of natural logic
relations from simulated data and from the WordNet noun graph. The overall
positive results are promising for the future of learned distributed
representations in the applied modeling of logical semantics.
Open-set Person Re-identification
Authors: Shengcai Liao, Zhipeng Mo, Jianqing Zhu, Yang Hu, Stan Z. Li
Date: 2014-08-05 00:00:00
Tasks: Metric Learning, Person Re-Identification

Person re-identification is becoming a hot research for developing both
machine learning algorithms and video surveillance applications. The task of
person re-identification is to determine which person in a gallery has the same
identity to a probe image. This task basically assumes that the subject of the
probe image belongs to the gallery, that is, the gallery contains this person.
However, in practical applications such as searching a suspect in a video, this
assumption is usually not true. In this paper, we consider the open-set person
re-identification problem, which includes two sub-tasks, detection and
identification. The detection sub-task is to determine the presence of the
probe subject in the gallery, and the identification sub-task is to determine
which person in the gallery has the same identity as the accepted probe. We
present a database collected from a video surveillance setting of 6 cameras,
with 200 persons and 7,413 images segmented. Based on this database, we develop
a benchmark protocol for evaluating the performance under the open-set person
re-identification scenario. Several popular metric learning algorithms for
person re-identification have been evaluated as baselines. From the baseline
performance, we observe that the open-set person re-identification problem is
still largely unresolved, thus further attention and effort is needed.
Separating the Real from the Synthetic: Minutiae Histograms as Fingerprints of Fingerprints
Authors: Carsten Gottschlich, Stephan Huckemann
Date: 2013-04-19 00:00:00
Tasks: Translation

In this study we show that by the current state-of-the-art synthetically
generated fingerprints can easily be discriminated from real fingerprints. We
propose a method based on second order extended minutiae histograms (MHs) which
can distinguish between real and synthetic prints with very high accuracy. MHs
provide a fixed-length feature vector for a fingerprint which are invariant
under rotation and translation. This 'test of realness' can be applied to
synthetic fingerprints produced by any method. In this work, tests are
conducted on the 12 publicly available databases of FVC2000, FVC2002 and
FVC2004 which are well established benchmarks for evaluating the performance of
fingerprint recognition algorithms; 3 of these 12 databases consist of
artificial fingerprints generated by the SFinGe software. Additionally, we
evaluate the discriminative performance on a database of synthetic fingerprints
generated by the software of Bicz versus real fingerprint images. We conclude
with suggestions for the improvement of synthetic fingerprint generation.
Complexity Issues and Randomization Strategies in Frank-Wolfe Algorithms for Machine Learning
Authors: Emanuele Frandi, Ricardo Nanculef, Johan Suykens
Date: 2014-10-15 00:00:00
Tasks: BIG-bench Machine Learning

Frank-Wolfe algorithms for convex minimization have recently gained
considerable attention from the Optimization and Machine Learning communities,
as their properties make them a suitable choice in a variety of applications.
However, as each iteration requires to optimize a linear model, a clever
implementation is crucial to make such algorithms viable on large-scale
datasets. For this purpose, approximation strategies based on a random sampling
have been proposed by several researchers. In this work, we perform an
experimental study on the effectiveness of these techniques, analyze possible
alternatives and provide some guidelines based on our results.
Online Tracking of Skin Colour Regions Against a Complex Background
Authors: Subhadip Basu, S. Chakraborty, K. Mukherjee, S. K. Pandit
Date: 2014-10-15 00:00:00

Online tracking of human activity against a complex background is a
challenging task for many applications. In this paper, we have developed a
robust technique for localizing skin colour regions from unconstrained image
frames. A simple and fast segmentation algorithm is used to train a multiplayer
perceptron (MLP) for detection of skin colours. Stepper motors are synchronized
with the MLP to track the movement of the skin colour regions.
A two-pass fuzzy-geno approach to pattern classification
Authors: Subhadip Basu, Mahantapas Kundu, Mita Nasipuri, Dipak Kumar Basu
Date: 2014-10-15 00:00:00
Tasks: Classification, General Classification, Vocal Bursts Valence Prediction

The work presents an extension of the fuzzy approach to 2-D shape recognition
[1] through refinement of initial or coarse classification decisions under a
two pass approach. In this approach, an unknown pattern is classified by
refining possible classification decisions obtained through coarse
classification of the same. To build a fuzzy model of a pattern class
horizontal and vertical fuzzy partitions on the sample images of the class are
optimized using genetic algorithm. To make coarse classification decisions
about an unknown pattern, the fuzzy representation of the pattern is compared
with models of all pattern classes through a specially designed similarity
measure. Coarse classification decisions are refined in the second pass to
obtain the final classification decision of the unknown pattern. To do so,
optimized horizontal and vertical fuzzy partitions are again created on certain
regions of the image frame, specific to each group of similar type of pattern
classes. It is observed through experiments that the technique improves the
overall recognition rate from 86.2%, in the first pass, to 90.4% after the
second pass, with 500 training samples of handwritten digits.
Online interpretation of numeric sign language using 2-d skeletal model
Authors: Subhadip Basu, S. Dey, K. Mukherjee, T. S. Jana
Date: 2014-10-15 00:00:00

Gesturing is one of the natural modes of human communication. Signs produced
by gestures can have a basic meaning coupled with additional information that
is layered over the basic meaning of the sign. Sign language is an important
example of communicative gestures that are highly structured and well accepted
across the world as a communication medium for deaf and dumb. In this paper, an
online recognition scheme is proposed to interpret the standard numeric sign
language comprising of 10 basic hand symbols. A web camera is used to capture
the real time hand movements as input to the system. The basic meaning of the
hand gesture is extracted from the input data frame by analysing the shape of
the hand, considering its orientation, movement and location to be fixed. The
input hand shape is processed to identify the palm structure, fingertips and
their relative positions and the presence of the extended thumb. A
2-dimensional skeletal model is generated from the acquired shape information
to represent and subsequently interpret the basic meaning of the hand gesture.
Thompson sampling with the online bootstrap
Authors: Dean Eckles, Maurits Kaptein
Date: 2014-10-15 00:00:00
Tasks: Thompson Sampling

Thompson sampling provides a solution to bandit problems in which new
observations are allocated to arms with the posterior probability that an arm
is optimal. While sometimes easy to implement and asymptotically optimal,
Thompson sampling can be computationally demanding in large scale bandit
problems, and its performance is dependent on the model fit to the observed
data. We introduce bootstrap Thompson sampling (BTS), a heuristic method for
solving bandit problems which modifies Thompson sampling by replacing the
posterior distribution used in Thompson sampling by a bootstrap distribution.
We first explain BTS and show that the performance of BTS is competitive to
Thompson sampling in the well-studied Bernoulli bandit case. Subsequently, we
detail why BTS using the online bootstrap is more scalable than regular
Thompson sampling, and we show through simulation that BTS is more robust to a
misspecified error distribution. BTS is an appealing modification of Thompson
sampling, especially when samples from the posterior are otherwise not
available or are costly.
Shape and Color Object Tracking for Real-Time Robotic Navigation
Authors: Haythem Ghazouani
Date: 2014-10-15 00:00:00
Tasks: General Classification, Object Tracking

This paper presents a real-time approach for single-colored ball detection
and tracking. The approach consists of two main phases. In a first offline
calibration phase, the intrinsic parameters of the camera and the radial
distortion are estimated, and a classification of colors is learned from a
sample image of colored balls. The second phase consists of four main steps:
(1) color segmentation of the input image into several regions based on the
offline classification, (2) robust estimation of the circle parameters (3)
refinement of the circle parameters, and (4) ball tracking. The experimental
results showed that the approach presents a good compromise between suitability
for real-time navigation and robustness to occlusions, background congestion
and colors interference in the scene.
Probabilistic Interpretation of Linear Solvers
Authors: Philipp Hennig
Date: 2014-02-10 00:00:00

This manuscript proposes a probabilistic framework for algorithms that
iteratively solve unconstrained linear problems $Bx = b$ with positive definite
$B$ for $x$. The goal is to replace the point estimates returned by existing
methods with a Gaussian posterior belief over the elements of the inverse of
$B$, which can be used to estimate errors. Recent probabilistic interpretations
of the secant family of quasi-Newton optimization algorithms are extended.
Combined with properties of the conjugate gradient algorithm, this leads to
uncertainty-calibrated methods with very limited cost overhead over conjugate
gradients, a self-contained novel interpretation of the quasi-Newton and
conjugate gradient algorithms, and a foundation for new nonlinear optimization
methods.
A Logic-based Approach to Generatively Defined Discriminative Modeling
Authors: Taisuke Sato, Keiichi Kubota, Yoshitaka Kameya
Date: 2014-10-15 00:00:00

Conditional random fields (CRFs) are usually specified by graphical models
but in this paper we propose to use probabilistic logic programs and specify
them generatively. Our intension is first to provide a unified approach to CRFs
for complex modeling through the use of a Turing complete language and second
to offer a convenient way of realizing generative-discriminative pairs in
machine learning to compare generative and discriminative models and choose the
best model. We implemented our approach as the D-PRISM language by modifying
PRISM, a logic-based probabilistic modeling language for generative modeling,
while exploiting its dynamic programming mechanism for efficient probability
computation. We tested D-PRISM with logistic regression, a linear-chain CRF and
a CRF-CFG and empirically confirmed their excellent discriminative performance
compared to their generative counterparts, i.e.\ naive Bayes, an HMM and a
PCFG. We also introduced new CRF models, CRF-BNCs and CRF-LCGs. They are CRF
versions of Bayesian network classifiers and probabilistic left-corner grammars
respectively and easily implementable in D-PRISM. We empirically showed that
they outperform their generative counterparts as expected.
Detection of Salient Regions in Crowded Scenes
Authors: Mei Kuan Lim, Chee Seng Chan, Dorothy Monekosso, Paolo Remagnino
Date: 2014-10-15 00:00:00

The increasing number of cameras and a handful of human operators to monitor
the video inputs from hundreds of cameras leave the system ill equipped to
fulfil the task of detecting anomalies. Thus, there is a dire need to
automatically detect regions that require immediate attention for a more
effective and proactive surveillance. We propose a framework that utilises the
temporal variations in the flow field of a crowd scene to automatically detect
salient regions, while eliminating the need to have prior knowledge of the
scene or training. We deem the flow fields to be a dynamic system and adopt the
stability theory of dynamical systems, to determine the motion dynamics within
a given area. In the context of this work, salient regions refer to areas with
high motion dynamics, where points in a particular region are unstable.
Experimental results on public, crowd scenes have shown the effectiveness of
the proposed method in detecting salient regions which correspond to unstable
flow, occlusions, bottlenecks, entries and exits.
Spotting Suspicious Link Behavior with fBox: An Adversarial Perspective
Authors: Neil Shah, Alex Beutel, Brian Gallagher, Christos Faloutsos
Date: 2014-10-15 00:00:00

How can we detect suspicious users in large online networks? Online
popularity of a user or product (via follows, page-likes, etc.) can be
monetized on the premise of higher ad click-through rates or increased sales.
Web services and social networks which incentivize popularity thus suffer from
a major problem of fake connections from link fraudsters looking to make a
quick buck. Typical methods of catching this suspicious behavior use spectral
techniques to spot large groups of often blatantly fraudulent (but sometimes
honest) users. However, small-scale, stealthy attacks may go unnoticed due to
the nature of low-rank eigenanalysis used in practice.
  In this work, we take an adversarial approach to find and prove claims about
the weaknesses of modern, state-of-the-art spectral methods and propose fBox,
an algorithm designed to catch small-scale, stealth attacks that slip below the
radar. Our algorithm has the following desirable properties: (a) it has
theoretical underpinnings, (b) it is shown to be highly effective on real data
and (c) it is scalable (linear on the input size). We evaluate fBox on a large,
public 41.7 million node, 1.5 billion edge who-follows-whom social graph from
Twitter in 2010 and with high precision identify many suspicious accounts which
have persisted without suspension even to this day.
High Order Structure Descriptors for Scene Images
Authors: Wenya Zhu, Xiankai Lu, Tao Xu, Ziyi Zhao
Date: 2014-10-15 00:00:00
Tasks: Scene Classification, Vocal Bursts Intensity Prediction

Structure information is ubiquitous in natural scene images and it plays an
important role in scene representation. In this paper, third order structure
statistics (TOSS) and fourth order structure statistics (FOSS) are exploited to
encode higher order structure information. Afterwards, based on the radial and
normal slice of TOSS and FOSS, we propose the high order structure feature:
third order structure feature (TOSF) and fourth order structure feature (FOSF).
It is well known that scene images are well characterized by particular
arrangements of their local structures, we divide the scene image into the
non-overlapping sub-regions and compute the proposed higher order structural
features among them. Then a scene classification is performed by using SVM
classifier with these higher order structure features. The experimental results
show that higher order structure statistics can deliver image structure
information well and its spatial envelope has strong discriminative ability.
Efficient Image Categorization with Sparse Fisher Vector
Authors: Xiankai Lu, Zheng Fang, Tao Xu, Haiting Zhang, Hongya Tuo
Date: 2014-10-15 00:00:00
Tasks: Image Categorization, Object Recognition

In object recognition, Fisher vector (FV) representation is one of the
state-of-art image representations ways at the expense of dense, high
dimensional features and increased computation time. A simplification of FV is
attractive, so we propose Sparse Fisher vector (SFV). By incorporating locality
strategy, we can accelerate the Fisher coding step in image categorization
which is implemented from a collective of local descriptors. Combining with
pooling step, we explore the relationship between coding step and pooling step
to give a theoretical explanation about SFV. Experiments on benchmark datasets
have shown that SFV leads to a speedup of several-fold of magnitude compares
with FV, while maintaining the categorization performance. In addition, we
demonstrate how SFV preserves the consistence in representation of similar
local features.