pwc-archive/papers-with-abstracts huggingface dataset:
Contextual Hypergraph Modelling for Salient Object Detection
Authors: Xi Li, Yao Li, Chunhua Shen, Anthony Dick, Anton Van Den Hengel
Date: 2013-10-22 00:00:00
Tasks: Object, object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection

Salient object detection aims to locate objects that capture human attention
within images. Previous approaches often pose this as a problem of image
contrast analysis. In this work, we model an image as a hypergraph that
utilizes a set of hyperedges to capture the contextual properties of image
pixels or regions. As a result, the problem of salient object detection becomes
one of finding salient vertices and hyperedges in the hypergraph. The main
advantage of hypergraph modeling is that it takes into account each pixel's (or
region's) affinity with its neighborhood as well as its separation from image
background. Furthermore, we propose an alternative approach based on
center-versus-surround contextual contrast analysis, which performs salient
object detection by optimizing a cost-sensitive support vector machine (SVM)
objective function. Experimental results on four challenging datasets
demonstrate the effectiveness of the proposed approaches against the
state-of-the-art approaches to salient object detection.
Determination, Calculation and Representation of the Upper and Lower Sealing Zones During Virtual Stenting of Aneurysms
Authors: Jan Egger, Miriam H. A. Bauer, Stefan Großkopf, Christina Biermann, Bernd Freisleben, Christopher Nimsky
Date: 2013-10-21 00:00:00

In this contribution, a novel method for stent simulation in preoperative
computed tomography angiography (CTA) acquisitions of patients is presented
where the sealing zones are automatically calculated and visualized. The method
is eligible for non-bifurcated and bifurcated stents (Y-stents). Results of the
proposed stent simulation with an automatic calculation of the sealing zones
for specific diseases (abdominal aortic aneurysms (AAA), thoracic aortic
aneurysms (TAA), iliac aneurysms) are presented. The contribution is organized
as follows. Section 2 presents the proposed approach. In Section 3,
experimental results are discussed. Section 4 concludes the contribution and
outlines areas for future work.
A Kernel for Hierarchical Parameter Spaces
Authors: Frank Hutter, Michael A. Osborne
Date: 2013-10-21 00:00:00

We define a family of kernels for mixed continuous/discrete hierarchical
parameter spaces and show that they are positive definite.
Distributed parameter estimation of discrete hierarchical models via marginal likelihoods
Authors: Helene Massam, Nanwei Wang
Date: 2013-10-21 00:00:00
Tasks: parameter estimation

We consider discrete graphical models Markov with respect to a graph $G$ and
propose two distributed marginal methods to estimate the maximum likelihood
estimate of the canonical parameter of the model. Both methods are based on a
relaxation of the marginal likelihood obtained by considering the density of
the variables represented by a vertex $v$ of $G$ and a neighborhood. The two
methods differ by the size of the neighborhood of $v$. We show that the
estimates are consistent and that those obtained with the larger neighborhood
have smaller asymptotic variance than the ones obtained through the smaller
neighborhood.
Robust Near-Separable Nonnegative Matrix Factorization Using Linear Optimization
Authors: Nicolas Gillis, Robert Luce
Date: 2013-02-18 00:00:00

Nonnegative matrix factorization (NMF) has been shown recently to be
tractable under the separability assumption, under which all the columns of the
input data matrix belong to the convex cone generated by only a few of these
columns. Bittorf, Recht, R\'e and Tropp (`Factoring nonnegative matrices with
linear programs', NIPS 2012) proposed a linear programming (LP) model, referred
to as Hottopixx, which is robust under any small perturbation of the input
matrix. However, Hottopixx has two important drawbacks: (i) the input matrix
has to be normalized, and (ii) the factorization rank has to be known in
advance. In this paper, we generalize Hottopixx in order to resolve these two
drawbacks, that is, we propose a new LP model which does not require
normalization and detects the factorization rank automatically. Moreover, the
new LP model is more flexible, significantly more tolerant to noise, and can
easily be adapted to handle outliers and other noise models. Finally, we show
on several synthetic datasets that it outperforms Hottopixx while competing
favorably with two state-of-the-art methods.
Devnagari Handwritten Numeral Recognition using Geometric Features and Statistical Combination Classifier
Authors: Vikas J. Dongre, Vijay H. Mankar
Date: 2013-10-21 00:00:00
Tasks: General Classification

This paper presents a Devnagari Numerical recognition method based on
statistical discriminant functions. 17 geometric features based on pixel
connectivity, lines, line directions, holes, image area, perimeter,
eccentricity, solidity, orientation etc. are used for representing the
numerals. Five discriminant functions viz. Linear, Quadratic, Diaglinear,
Diagquadratic and Mahalanobis distance are used for classification. 1500
handwritten numerals are used for training. Another 1500 handwritten numerals
are used for testing. Experimental results show that Linear, Quadratic and
Mahalanobis discriminant functions provide better results. Results of these
three Discriminants are fed to a majority voting type Combination classifier.
It is found that Combination classifier offers better results over individual
classifiers.
Least Squares Revisited: Scalable Approaches for Multi-class Prediction
Authors: Alekh Agarwal, Sham M. Kakade, Nikos Karampatziakis, Le Song, Gregory Valiant
Date: 2013-10-07 00:00:00
Tasks: Prediction

This work provides simple algorithms for multi-class (and multi-label)
prediction in settings where both the number of examples n and the data
dimension d are relatively large. These robust and parameter free algorithms
are essentially iterative least-squares updates and very versatile both in
theory and in practice. On the theoretical front, we present several variants
with convergence guarantees. Owing to their effective use of second-order
structure, these algorithms are substantially better than first-order methods
in many practical scenarios. On the empirical side, we present a scalable
stagewise variant of our approach, which achieves dramatic computational
speedups over popular optimization packages such as Liblinear and Vowpal Wabbit
on standard datasets (MNIST and CIFAR-10), while attaining state-of-the-art
accuracies.
Convex Discriminative Multitask Clustering
Authors: Xiao-Lei Zhang
Date: 2013-03-08 00:00:00
Tasks: Clustering

Multitask clustering tries to improve the clustering performance of multiple
tasks simultaneously by taking their relationship into account. Most existing
multitask clustering algorithms fall into the type of generative clustering,
and none are formulated as convex optimization problems. In this paper, we
propose two convex Discriminative Multitask Clustering (DMTC) algorithms to
address the problems. Specifically, we first propose a Bayesian DMTC framework.
Then, we propose two convex DMTC objectives within the framework. The first
one, which can be seen as a technical combination of the convex multitask
feature learning and the convex Multiclass Maximum Margin Clustering (M3C),
aims to learn a shared feature representation. The second one, which can be
seen as a combination of the convex multitask relationship learning and M3C,
aims to learn the task relationship. The two objectives are solved in a uniform
procedure by the efficient cutting-plane algorithm. Experimental results on a
toy problem and two benchmark datasets demonstrate the effectiveness of the
proposed algorithms.
Towards Application of the RBNK Model
Authors: Larry Bull
Date: 2013-10-21 00:00:00
Tasks: model

The computational modeling of genetic regulatory networks is now common
place, either by fitting a system to experimental data or by exploring the
behaviour of abstract systems with the aim of identifying underlying
principles. This paper presents an approach to the latter, considering the
response to environmental changes of a well-known model placed upon tunable
fitness landscapes. The effects on genome size and gene connectivity are
explored.
Ship Detection and Segmentation using Image Correlation
Authors: Alexander Kadyrov, Hui Yu, Honghai Liu
Date: 2013-10-21 00:00:00

There have been intensive research interests in ship detection and
segmentation due to high demands on a wide range of civil applications in the
last two decades. However, existing approaches, which are mainly based on
statistical properties of images, fail to detect smaller ships and boats.
Specifically, known techniques are not robust enough in view of inevitable
small geometric and photometric changes in images consisting of ships. In this
paper a novel approach for ship detection is proposed based on correlation of
maritime images. The idea comes from the observation that a fine pattern of the
sea surface changes considerably from time to time whereas the ship appearance
basically keeps unchanged. We want to examine whether the images have a common
unaltered part, a ship in this case. To this end, we developed a method -
Focused Correlation (FC) to achieve robustness to geometric distortions of the
image content. Various experiments have been conducted to evaluate the
effectiveness of the proposed approach.
Stable and robust sampling strategies for compressive imaging
Authors: Felix Krahmer, Rachel Ward
Date: 2012-10-08 00:00:00
Tasks: Compressive Sensing, Image Reconstruction

In many signal processing applications, one wishes to acquire images that are
sparse in transform domains such as spatial finite differences or wavelets
using frequency domain samples. For such applications, overwhelming empirical
evidence suggests that superior image reconstruction can be obtained through
variable density sampling strategies that concentrate on lower frequencies. The
wavelet and Fourier transform domains are not incoherent because low-order
wavelets and low-order frequencies are correlated, so compressive sensing
theory does not immediately imply sampling strategies and reconstruction
guarantees. In this paper we turn to a more refined notion of coherence -- the
so-called local coherence -- measuring for each sensing vector separately how
correlated it is to the sparsity basis. For Fourier measurements and Haar
wavelet sparsity, the local coherence can be controlled and bounded explicitly,
so for matrices comprised of frequencies sampled from a suitable inverse square
power-law density, we can prove the restricted isometry property with
near-optimal embedding dimensions. Consequently, the variable-density sampling
strategy we provide allows for image reconstructions that are stable to
sparsity defects and robust to measurement noise. Our results cover both
reconstruction by $\ell_1$-minimization and by total variation minimization.
The local coherence framework developed in this paper should be of independent
interest in sparse recovery problems more generally, as it implies that for
optimal sparse recovery results, it suffices to have bounded \emph{average}
coherence from sensing basis to sparsity basis -- as opposed to bounded maximal
coherence -- as long as the sampling strategy is adapted accordingly.
A practical approach to ontology-enabled control systems for astronomical instrumentation
Authors: Wim Pessemier, Gert Raskin, Hans Van Winckel, Geert Deconinck, Philippe Saey
Date: 2013-10-21 00:00:00

Even though modern service-oriented and data-oriented architectures promise
to deliver loosely coupled control systems, they are inherently brittle as they
commonly depend on a priori agreed interfaces and data models. At the same
time, the Semantic Web and a whole set of accompanying standards and tools are
emerging, advocating ontologies as the basis for knowledge exchange. In this
paper we aim to identify a number of key ideas from the myriad of
knowledge-based practices that can readily be implemented by control systems
today. We demonstrate with a practical example (a three-channel imager for the
Mercator Telescope) how ontologies developed in the Web Ontology Language (OWL)
can serve as a meta-model for our instrument, covering as many engineering
aspects of the project as needed. We show how a concrete system model can be
built on top of this meta-model via a set of Domain Specific Languages (DSLs),
supporting both formal verification and the generation of software and
documentation artifacts. Finally we reason how the available semantics can be
exposed at run-time by adding a "semantic layer" that can be browsed, queried,
monitored etc. by any OPC UA-enabled client.
Multi-Task Regularization with Covariance Dictionary for Linear Classifiers
Authors: Fanyi Xiao, Ruikun Luo, Zhiding Yu
Date: 2013-10-21 00:00:00
Tasks: Transfer Learning, valid

In this paper we propose a multi-task linear classifier learning problem
called D-SVM (Dictionary SVM). D-SVM uses a dictionary of parameter covariance
shared by all tasks to do multi-task knowledge transfer among different tasks.
We formally define the learning problem of D-SVM and show two interpretations
of this problem, from both the probabilistic and kernel perspectives. From the
probabilistic perspective, we show that our learning formulation is actually a
MAP estimation on all optimization variables. We also show its equivalence to a
multiple kernel learning problem in which one is trying to find a re-weighting
kernel for features from a dictionary of basis (despite the fact that only
linear classifiers are learned). Finally, we describe an alternative
optimization scheme to minimize the objective function and present empirical
studies to valid our algorithm.
Bayesian Extensions of Kernel Least Mean Squares
Authors: Il Memming Park, Sohan Seth, Steven Van Vaerenbergh
Date: 2013-10-20 00:00:00

The kernel least mean squares (KLMS) algorithm is a computationally efficient
nonlinear adaptive filtering method that "kernelizes" the celebrated (linear)
least mean squares algorithm. We demonstrate that the least mean squares
algorithm is closely related to the Kalman filtering, and thus, the KLMS can be
interpreted as an approximate Bayesian filtering method. This allows us to
systematically develop extensions of the KLMS by modifying the underlying
state-space and observation models. The resulting extensions introduce many
desirable properties such as "forgetting", and the ability to learn from
discrete data, while retaining the computational simplicity and time complexity
of the original algorithm.
Graph-Based Approaches to Clustering Network-Constrained Trajectory Data
Authors: Mohamed Khalil El Mahrsi, Fabrice Rossi
Date: 2013-10-19 00:00:00
Tasks: Clustering, Community Detection

Clustering trajectory data attracted considerable attention in the last few
years. Most of prior work assumed that moving objects can move freely in an
euclidean space and did not consider the eventual presence of an underlying
road network and its influence on evaluating the similarity between
trajectories. In this paper, we present an approach to clustering such
network-constrained trajectory data. More precisely we aim at discovering
groups of road segments that are often travelled by the same trajectories. To
achieve this end, we model the interactions between segments w.r.t. their
similarity as a weighted graph to which we apply a community detection
algorithm to discover meaningful clusters. We showcase our proposition through
experimental results obtained on synthetic datasets.
Image Restoration using Total Variation with Overlapping Group Sparsity
Authors: Jun Liu, Ting-Zhu Huang, Ivan W. Selesnick, Xiao-Guang Lv, Po-Yu Chen
Date: 2013-10-13 00:00:00
Tasks: Image Restoration

Image restoration is one of the most fundamental issues in imaging science.
Total variation (TV) regularization is widely used in image restoration
problems for its capability to preserve edges. In the literature, however, it
is also well known for producing staircase-like artifacts. Usually, the
high-order total variation (HTV) regularizer is an good option except its
over-smoothing property. In this work, we study a minimization problem where
the objective includes an usual $l_2$ data-fidelity term and an overlapping
group sparsity total variation regularizer which can avoid staircase effect and
allow edges preserving in the restored image. We also proposed a fast algorithm
for solving the corresponding minimization problem and compare our method with
the state-of-the-art TV based methods and HTV based method. The numerical
experiments illustrate the efficiency and effectiveness of the proposed method
in terms of PSNR, relative error and computing time.
Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods
Authors: Gustavo Camps-Valls, Devis Tuia, Lorenzo Bruzzone, Jón Atli Benediktsson
Date: 2013-10-18 00:00:00
Tasks: Active Learning, Classification Of Hyperspectral Images, General Classification, Hyperspectral Image Classification, image-classification, Image Classification, Remote Sensing Image Classification

Hyperspectral images show similar statistical properties to natural grayscale
or color photographic images. However, the classification of hyperspectral
images is more challenging because of the very high dimensionality of the
pixels and the small number of labeled examples typically available for
learning. These peculiarities lead to particular signal processing problems,
mainly characterized by indetermination and complex manifolds. The framework of
statistical learning has gained popularity in the last decade. New methods have
been presented to account for the spatial homogeneity of images, to include
user's interaction via active learning, to take advantage of the manifold
structure with semisupervised learning, to extract and encode invariances, or
to adapt classifiers and image representations to unseen yet similar scenes.
This tutuorial reviews the main advances for hyperspectral remote sensing image
classification through illustrative examples.
Duality between subgradient and conditional gradient methods
Authors: Francis Bach
Date: 2012-11-27 00:00:00
Tasks: BIG-bench Machine Learning

Given a convex optimization problem and its dual, there are many possible
first-order algorithms. In this paper, we show the equivalence between mirror
descent algorithms and algorithms generalizing the conditional gradient method.
This is done through convex duality, and implies notably that for certain
problems, such as for supervised machine learning problems with non-smooth
losses or problems regularized by non-smooth regularizers, the primal
subgradient method and the dual conditional gradient method are formally
equivalent. The dual interpretation leads to a form of line search for mirror
descent, as well as guarantees of convergence for primal-dual certificates.
Regularization in Relevance Learning Vector Quantization Using l one Norms
Authors: Martin Riedel, Marika Kästner, Fabrice Rossi, Thomas Villmann
Date: 2013-10-18 00:00:00
Tasks: General Classification, Quantization

We propose in this contribution a method for l one regularization in
prototype based relevance learning vector quantization (LVQ) for sparse
relevance profiles. Sparse relevance profiles in hyperspectral data analysis
fade down those spectral bands which are not necessary for classification. In
particular, we consider the sparsity in the relevance profile enforced by LASSO
optimization. The latter one is obtained by a gradient learning scheme using a
differentiable parametrized approximation of the $l_{1}$-norm, which has an
upper error bound. We extend this regularization idea also to the matrix
learning variant of LVQ as the natural generalization of relevance learning.
Kernel Multivariate Analysis Framework for Supervised Subspace Learning: A Tutorial on Linear and Kernel Multivariate Methods
Authors: Jerónimo Arenas-García, Kaare Brandt Petersen, Gustavo Camps-Valls, Lars Kai Hansen
Date: 2013-10-18 00:00:00
Tasks: Dimensionality Reduction, General Classification

Feature extraction and dimensionality reduction are important tasks in many
fields of science dealing with signal processing and analysis. The relevance of
these techniques is increasing as current sensory devices are developed with
ever higher resolution, and problems involving multimodal data sources become
more common. A plethora of feature extraction methods are available in the
literature collectively grouped under the field of Multivariate Analysis (MVA).
This paper provides a uniform treatment of several methods: Principal Component
Analysis (PCA), Partial Least Squares (PLS), Canonical Correlation Analysis
(CCA) and Orthonormalized PLS (OPLS), as well as their non-linear extensions
derived by means of the theory of reproducing kernel Hilbert spaces. We also
review their connections to other methods for classification and statistical
dependence estimation, and introduce some recent developments to deal with the
extreme cases of large-scale and low-sized problems. To illustrate the wide
applicability of these methods in both classification and regression problems,
we analyze their performance in a benchmark of publicly available data sets,
and pay special attention to specific real applications involving audio
processing for music genre prediction and hyperspectral satellite images for
Earth and climate monitoring.
On the Suitable Domain for SVM Training in Image Coding
Authors: Gustavo Camps-Valls, Juan Gutiérrez, Gabriel Gómez-Pérez, Jesús Malo
Date: 2013-10-18 00:00:00

Conventional SVM-based image coding methods are founded on independently
restricting the distortion in every image coefficient at some particular image
representation. Geometrically, this implies allowing arbitrary signal
distortions in an $n$-dimensional rectangle defined by the
$\varepsilon$-insensitivity zone in each dimension of the selected image
representation domain. Unfortunately, not every image representation domain is
well-suited for such a simple, scalar-wise, approach because statistical and/or
perceptual interactions between the coefficients may exist. These interactions
imply that scalar approaches may induce distortions that do not follow the
image statistics and/or are perceptually annoying. Taking into account these
relations would imply using non-rectangular $\varepsilon$-insensitivity regions
(allowing coupled distortions in different coefficients), which is beyond the
conventional SVM formulation.
  In this paper, we report a condition on the suitable domain for developing
efficient SVM image coding schemes. We analytically demonstrate that no linear
domain fulfills this condition because of the statistical and perceptual
inter-coefficient relations that exist in these domains. This theoretical
result is experimentally confirmed by comparing SVM learning in previously
reported linear domains and in a recently proposed non-linear perceptual domain
that simultaneously reduces the statistical and perceptual relations (so it is
closer to fulfilling the proposed condition). These results highlight the
relevance of an appropriate choice of the image representation before SVM
learning.