pwc-archive/papers-with-abstracts huggingface dataset:
Cube-Cut: Vertebral Body Segmentation in MRI-Data through Cubic-Shaped Divergences
Authors: Robert Schwarzenberg, Bernd Freisleben, Christopher Nimsky, Jan Egger
Date: 2014-04-17 00:00:00
Tasks: graph construction

In this article, we present a graph-based method using a cubic template for
volumetric segmentation of vertebrae in magnetic resonance imaging (MRI)
acquisitions. The user can define the degree of deviation from a regular cube
via a smoothness value Delta. The Cube-Cut algorithm generates a directed graph
with two terminal nodes (s-t-network), where the nodes of the graph correspond
to a cubic-shaped subset of the image's voxels. The weightings of the graph's
terminal edges, which connect every node with a virtual source s or a virtual
sink t, represent the affinity of a voxel to the vertebra (source) and to the
background (sink). Furthermore, a set of infinite weighted and non-terminal
edges implements the smoothness term. After graph construction, a minimal
s-t-cut is calculated within polynomial computation time, which splits the
nodes into two disjoint units. Subsequently, the segmentation result is
determined out of the source-set. A quantitative evaluation of a C++
implementation of the algorithm resulted in an average Dice Similarity
Coefficient (DSC) of 81.33% and a running time of less than a minute.
Axioms for graph clustering quality functions
Authors: Twan van Laarhoven, Elena Marchiori
Date: 2013-08-15 00:00:00
Tasks: Clustering, Community Detection, Graph Clustering

We investigate properties that intuitively ought to be satisfied by graph
clustering quality functions, that is, functions that assign a score to a
clustering of a graph. Graph clustering, also known as network community
detection, is often performed by optimizing such a function. Two axioms
tailored for graph clustering quality functions are introduced, and the four
axioms introduced in previous work on distance based clustering are
reformulated and generalized for the graph setting. We show that modularity, a
standard quality function for graph clustering, does not satisfy all of these
six properties. This motivates the derivation of a new family of quality
functions, adaptive scale modularity, which does satisfy the proposed axioms.
Adaptive scale modularity has two parameters, which give greater flexibility in
the kinds of clusterings that can be found. Standard graph clustering quality
functions, such as normalized cut and unnormalized cut, are obtained as special
cases of adaptive scale modularity.
  In general, the results of our investigation indicate that the considered
axiomatic framework covers existing `good' quality functions for graph
clustering, and can be used to derive an interesting new family of quality
functions.
Resolution-limit-free and local Non-negative Matrix Factorization quality functions for graph clustering
Authors: Twan van Laarhoven, Elena Marchiori
Date: 2014-07-22 00:00:00
Tasks: Clustering, Graph Clustering, graph partitioning

Many graph clustering quality functions suffer from a resolution limit, the
inability to find small clusters in large graphs. So called
resolution-limit-free quality functions do not have this limit. This property
was previously introduced for hard clustering, that is, graph partitioning.
  We investigate the resolution-limit-free property in the context of
Non-negative Matrix Factorization (NMF) for hard and soft graph clustering. To
use NMF in the hard clustering setting, a common approach is to assign each
node to its highest membership cluster. We show that in this case symmetric NMF
is not resolution-limit-free, but that it becomes so when hardness constraints
are used as part of the optimization. The resulting function is strongly linked
to the Constant Potts Model. In soft clustering, nodes can belong to more than
one cluster, with varying degrees of membership. In this setting
resolution-limit-free turns out to be too strong a property. Therefore we
introduce locality, which roughly states that changing one part of the graph
does not affect the clustering of other parts of the graph. We argue that this
is a desirable property, provide conditions under which NMF quality functions
are local, and propose a novel class of local probabilistic NMF quality
functions for soft graph clustering.
Confidence-Based Dynamic Classifier Combination For Mean-Shift Tracking
Authors: Ibrahim Saygin Topkaya, Hakan Erdogan
Date: 2011-07-29 00:00:00
Tasks: Object Tracking

We introduce a novel tracking technique which uses dynamic confidence-based
fusion of two different information sources for robust and efficient tracking
of visual objects. Mean-shift tracking is a popular and well known method used
in object tracking problems. Originally, the algorithm uses a similarity
measure which is optimized by shifting a search area to the center of a
generated weight image to track objects. Recent improvements on the original
mean-shift algorithm involves using a classifier that differentiates the object
from its surroundings. We adopt this classifier-based approach and propose an
application of a classifier fusion technique within this classifier-based
context in this work. We use two different classifiers, where one comes from a
background modeling method, to generate the weight image and we calculate
contributions of the classifiers dynamically using their confidences to
generate a final weight image to be used in tracking. The contributions of the
classifiers are calculated by using correlations between histograms of their
weight images and histogram of a defined ideal weight image in the previous
frame. We show with experiments that our dynamic combination scheme selects
good contributions for classifiers for different cases and improves tracking
accuracy significantly.
How good are detection proposals, really?
Authors: Jan Hosang, Rodrigo Benenson, Bernt Schiele
Date: 2014-06-26 00:00:00
Tasks: Object, object-detection, Object Detection

Current top performing Pascal VOC object detectors employ detection proposals
to guide the search for objects thereby avoiding exhaustive sliding window
search across images. Despite the popularity of detection proposals, it is
unclear which trade-offs are made when using them during object detection. We
provide an in depth analysis of ten object proposal methods along with four
baselines regarding ground truth annotation recall (on Pascal VOC 2007 and
ImageNet 2013), repeatability, and impact on DPM detector performance. Our
findings show common weaknesses of existing methods, and provide insights to
choose the most adequate method for different settings.
Approximate Regularization Path for Nuclear Norm Based H2 Model Reduction
Authors: Niclas Blomberg, Cristian R. Rojas, Bo Wahlberg
Date: 2014-07-22 00:00:00

This paper concerns model reduction of dynamical systems using the nuclear
norm of the Hankel matrix to make a trade-off between model fit and model
complexity. This results in a convex optimization problem where this trade-off
is determined by one crucial design parameter. The main contribution is a
methodology to approximately calculate all solutions up to a certain tolerance
to the model reduction problem as a function of the design parameter. This is
called the regularization path in sparse estimation and is a very important
tool in order to find the appropriate balance between fit and complexity. We
extend this to the more complicated nuclear norm case. The key idea is to
determine when to exactly calculate the optimal solution using an upper bound
based on the so-called duality gap. Hence, by solving a fixed number of
optimization problems the whole regularization path up to a given tolerance can
be efficiently computed. We illustrate this approach on some numerical
examples.
Multi-agents adaptive estimation and coverage control using Gaussian regression
Authors: Andrea Carron, Marco Todescato, Ruggero Carli, Luca Schenato, Gianluigi Pillonetto
Date: 2014-07-22 00:00:00
Tasks: regression

We consider a scenario where the aim of a group of agents is to perform the
optimal coverage of a region according to a sensory function. In particular,
centroidal Voronoi partitions have to be computed. The difficulty of the task
is that the sensory function is unknown and has to be reconstructed on line
from noisy measurements. Hence, estimation and coverage needs to be performed
at the same time. We cast the problem in a Bayesian regression framework, where
the sensory function is seen as a Gaussian random field. Then, we design a set
of control inputs which try to well balance coverage and estimation, also
discussing convergence properties of the algorithm. Numerical experiments show
the effectivness of the new approach.
Optimal designs for Lasso and Dantzig selector using Expander Codes
Authors: Yohann de Castro
Date: 2010-10-12 00:00:00

We investigate the high-dimensional regression problem using adjacency
matrices of unbalanced expander graphs. In this frame, we prove that the
$\ell_{2}$-prediction error and the $\ell_{1}$-risk of the lasso and the
Dantzig selector are optimal up to an explicit multiplicative constant. Thus we
can estimate a high-dimensional target vector with an error term similar to the
one obtained in a situation where one knows the support of the largest
coordinates in advance.
  Moreover, we show that these design matrices have an explicit restricted
eigenvalue. Precisely, they satisfy the restricted eigenvalue assumption and
the compatibility condition with an explicit constant.
  Eventually, we capitalize on the recent construction of unbalanced expander
graphs due to Guruswami, Umans, and Vadhan, to provide a deterministic
polynomial time construction of these design matrices.
Aggregation of local parametric candidates with exemplar-based occlusion handling for optical flow
Authors: Denis Fortun, Patrick Bouthemy, Charles Kervrann
Date: 2014-07-22 00:00:00
Tasks: Occlusion Handling, Optical Flow Estimation

Handling all together large displacements, motion details and occlusions
remains an open issue for reliable computation of optical flow in a video
sequence. We propose a two-step aggregation paradigm to address this problem.
The idea is to supply local motion candidates at every pixel in a first step,
and then to combine them to determine the global optical flow field in a second
step. We exploit local parametric estimations combined with patch
correspondences and we experimentally demonstrate that they are sufficient to
produce highly accurate motion candidates. The aggregation step is designed as
the discrete optimization of a global regularized energy. The occlusion map is
estimated jointly with the flow field throughout the two steps. We propose a
generic exemplar-based approach for occlusion filling with motion vectors. We
achieve state-of-the-art results in computer vision benchmarks, with
particularly significant improvements in the case of large displacements and
occlusions.
Tree-based iterated local search for Markov random fields with applications in image analysis
Authors: Truyen Tran, Dinh Phung, Svetha Venkatesh
Date: 2014-07-22 00:00:00
Tasks: Denoising, Image Denoising, Stereo Matching, Stereo Matching Hand

The \emph{maximum a posteriori} (MAP) assignment for general structure Markov
random fields (MRFs) is computationally intractable. In this paper, we exploit
tree-based methods to efficiently address this problem. Our novel method, named
Tree-based Iterated Local Search (T-ILS) takes advantage of the tractability of
tree-structures embedded within MRFs to derive strong local search in an ILS
framework. The method efficiently explores exponentially large neighborhood and
does so with limited memory without any requirement on the cost functions. We
evaluate the T-ILS in a simulation of Ising model and two real-world problems
in computer vision: stereo matching, image denoising. Experimental results
demonstrate that our methods are competitive against state-of-the-art rivals
with a significant computational gain.
Improved Onlooker Bee Phase in Artificial Bee Colony Algorithm
Authors: Sandeep Kumar, Vivek Kumar Sharma, Rajani Kumari
Date: 2014-07-22 00:00:00
Tasks: Diversity

Artificial Bee Colony (ABC) is a distinguished optimization strategy that can
resolve nonlinear and multifaceted problems. It is comparatively a
straightforward and modern population based probabilistic approach for
comprehensive optimization. In the vein of the other population based
algorithms, ABC is moreover computationally classy due to its slow nature of
search procedure. The solution exploration equation of ABC is extensively
influenced by a arbitrary quantity which helps in exploration at the cost of
exploitation of the better search space. In the solution exploration equation
of ABC due to the outsized step size the chance of skipping the factual
solution is high. Therefore, here this paper improve onlooker bee phase with
help of a local search strategy inspired by memetic algorithm to balance the
diversity and convergence capability of the ABC. The proposed algorithm is
named as Improved Onlooker Bee Phase in ABC (IoABC). It is tested over 12 well
known un-biased test problems of diverse complexities and two engineering
optimization problems; results show that the anticipated algorithm go one
better than the basic ABC and its recent deviations in a good number of the
experiments.
Global optimization using Lévy flights
Authors: Truyen Tran, Trung Thanh Nguyen, Hoang Linh Nguyen
Date: 2014-07-22 00:00:00
Tasks: global-optimization

This paper studies a class of enhanced diffusion processes in which random
walkers perform L\'evy flights and apply it for global optimization. L\'evy
flights offer controlled balance between exploitation and exploration. We
develop four optimization algorithms based on such properties. We compare new
algorithms with the well-known Simulated Annealing on hard test functions and
the results are very promising.
Learning Rich Features from RGB-D Images for Object Detection and Segmentation
Authors: Saurabh Gupta, Ross Girshick, Pablo Arbeláez, Jitendra Malik
Date: 2014-07-22 00:00:00
Tasks: Instance Segmentation, Object, object-detection, Object Detection, Object Detection In Indoor Scenes, Scene Segmentation, Semantic Segmentation

In this paper we study the problem of object detection for RGB-D images using
semantically rich image and depth features. We propose a new geocentric
embedding for depth images that encodes height above ground and angle with
gravity for each pixel in addition to the horizontal disparity. We demonstrate
that this geocentric embedding works better than using raw depth images for
learning feature representations with convolutional neural networks. Our final
object detection system achieves an average precision of 37.3%, which is a 56%
relative improvement over existing methods. We then focus on the task of
instance segmentation where we label pixels belonging to object instances found
by our detector. For this task, we propose a decision forest approach that
classifies pixels in the detection window as foreground or background using a
family of unary and binary tests that query shape and geocentric pose features.
Finally, we use the output from our object detectors in an existing superpixel
classification framework for semantic scene segmentation and achieve a 24%
relative improvement over current state-of-the-art for the object categories
that we study. We believe advances such as those represented in this paper will
facilitate the use of perception in fields like robotics.
Artificial Life and the Web: WebAL Comes of Age
Authors: Tim Taylor
Date: 2014-07-22 00:00:00
Tasks: Artificial Life, Survey

A brief survey is presented of the first 18 years of web-based Artificial
Life ("WebAL") research and applications, covering the period 1995-2013. The
survey is followed by a short discussion of common methodologies employed and
current technologies relevant to WebAL research. The paper concludes with a
quick look at what the future may hold for work in this exciting area.
Multichannel Compressive Sensing MRI Using Noiselet Encoding
Authors: Kamlesh Pawar, Gary F. Egan, Jingxin Zhang
Date: 2014-07-21 00:00:00
Tasks: Compressive Sensing

The incoherence between measurement and sparsifying transform matrices and
the restricted isometry property (RIP) of measurement matrix are two of the key
factors in determining the performance of compressive sensing (CS). In CS-MRI,
the randomly under-sampled Fourier matrix is used as the measurement matrix and
the wavelet transform is usually used as sparsifying transform matrix. However,
the incoherence between the randomly under-sampled Fourier matrix and the
wavelet matrix is not optimal, which can deteriorate the performance of CS-MRI.
Using the mathematical result that noiselets are maximally incoherent with
wavelets, this paper introduces the noiselet unitary bases as the measurement
matrix to improve the incoherence and RIP in CS-MRI, and presents a method to
design the pulse sequence for the noiselet encoding. This novel encoding scheme
is combined with the multichannel compressive sensing (MCS) framework to take
the advantage of multichannel data acquisition used in MRI scanners. An
empirical RIP analysis is presented to compare the multichannel noiselet and
multichannel Fourier measurement matrices in MCS. Simulations are presented in
the MCS framework to compare the performance of noiselet encoding
reconstructions and Fourier encoding reconstructions at different acceleration
factors. The comparisons indicate that multichannel noiselet measurement matrix
has better RIP than that of its Fourier counterpart, and that noiselet encoded
MCS-MRI outperforms Fourier encoded MCS-MRI in preserving image resolution and
can achieve higher acceleration factors. To demonstrate the feasibility of the
proposed noiselet encoding scheme, two pulse sequences with tailored spatially
selective RF excitation pulses was designed and implemented on a 3T scanner to
acquire the data in the noiselet domain from a phantom and a human brain.
Shape Primitive Histogram: A Novel Low-Level Face Representation for Face Recognition
Authors: Sheng Huang, Dan Yang, Haopeng Zhang, Luwen Huangfu, Xiaohong Zhang
Date: 2013-12-28 00:00:00
Tasks: Face Recognition

We further exploit the representational power of Haar wavelet and present a novel low-level face representation named Shape Primitives Histogram (SPH) for face recognition. Since human faces exist abundant shape features, we address the face representation issue from the perspective of the shape feature extraction. In our approach, we divide faces into a number of tiny shape fragments and reduce these shape fragments to several uniform atomic shape patterns called Shape Primitives. A convolution with Haar Wavelet templates is applied to each shape fragment to identify its belonging shape primitive. After that, we do a histogram statistic of shape primitives in each spatial local image patch for incorporating the spatial information. Finally, each face is represented as a feature vector via concatenating all the local histograms of shape primitives. Four popular face databases, namely ORL, AR, Yale-B and LFW-a databases, are employed to evaluate SPH and experimentally study the choices of the parameters. Extensive experimental results demonstrate that the proposed approach outperform the state-of-the-arts.
A metric for software vulnerabilities classification
Authors: Gabriele Modena
Date: 2012-12-15 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification, Relation

Vulnerability discovery and exploits detection are two wide areas of study in
software engineering. This preliminary work tries to combine existing methods
with machine learning techniques to define a metric classification of
vulnerable computer programs. First a feature set has been defined and later
two models have been tested against real world vulnerabilities. A relation
between the classifier choice and the features has also been outlined.
Predictive support recovery with TV-Elastic Net penalty and logistic regression: an application to structural MRI
Authors: Mathieu Dubois, Fouad Hadj-Selem, Tommy Lofstedt, Matthieu Perrot, Clara Fischer, Vincent Frouin, Edouard Duchesnay
Date: 2014-07-21 00:00:00
Tasks: Prognosis

The use of machine-learning in neuroimaging offers new perspectives in early
diagnosis and prognosis of brain diseases. Although such multivariate methods
can capture complex relationships in the data, traditional approaches provide
irregular (l2 penalty) or scattered (l1 penalty) predictive pattern with a very
limited relevance. A penalty like Total Variation (TV) that exploits the
natural 3D structure of the images can increase the spatial coherence of the
weight map. However, TV penalization leads to non-smooth optimization problems
that are hard to minimize. We propose an optimization framework that minimizes
any combination of l1, l2, and TV penalties while preserving the exact l1
penalty. This algorithm uses Nesterov's smoothing technique to approximate the
TV penalty with a smooth function such that the loss and the penalties are
minimized with an exact accelerated proximal gradient algorithm. We propose an
original continuation algorithm that uses successively smaller values of the
smoothing parameter to reach a prescribed precision while achieving the best
possible convergence rate. This algorithm can be used with other losses or
penalties. The algorithm is applied on a classification problem on the ADNI
dataset. We observe that the TV penalty does not necessarily improve the
prediction but provides a major breakthrough in terms of support recovery of
the predictive brain regions.
A Novel Hybrid Crossover based Artificial Bee Colony Algorithm for Optimization Problem
Authors: Sandeep Kumar, Vivek Kumar Sharma, Rajani Kumari
Date: 2014-07-21 00:00:00

Artificial bee colony (ABC) algorithm has proved its importance in solving a
number of problems including engineering optimization problems. ABC algorithm
is one of the most popular and youngest member of the family of population
based nature inspired meta-heuristic swarm intelligence method. ABC has been
proved its superiority over some other Nature Inspired Algorithms (NIA) when
applied for both benchmark functions and real world problems. The performance
of search process of ABC depends on a random value which tries to balance
exploration and exploitation phase. In order to increase the performance it is
required to balance the exploration of search space and exploitation of optimal
solution of the ABC. This paper outlines a new hybrid of ABC algorithm with
Genetic Algorithm. The proposed method integrates crossover operation from
Genetic Algorithm (GA) with original ABC algorithm. The proposed method is
named as Crossover based ABC (CbABC). The CbABC strengthens the exploitation
phase of ABC as crossover enhances exploration of search space. The CbABC
tested over four standard benchmark functions and a popular continuous
optimization problem.