pwc-archive/papers-with-abstracts huggingface dataset:
A statistical reduced-reference method for color image quality assessment
Authors: Mounir Omari, Mohammed El Hassouni, Abdelkaher Ait Abdelouahad, Hocine Cherifi
Date: 2014-11-27 00:00:00
Tasks: Image Quality Assessment

Although color is a fundamental feature of human visual perception, it has
been largely unexplored in the reduced-reference (RR) image quality assessment
(IQA) schemes. In this paper, we propose a natural scene statistic (NSS)
method, which efficiently uses this information. It is based on the statistical
deviation between the steerable pyramid coefficients of the reference color
image and the degraded one. We propose and analyze the multivariate generalized
Gaussian distribution (MGGD) to model the underlying statistics. In order to
quantify the degradation, we develop and evaluate two measures based
respectively on the Geodesic distance between two MGGDs and on the closed-form
of the Kullback Leibler divergence. We performed an extensive evaluation of
both metrics in various color spaces (RGB, HSV, CIELAB and YCrCb) using the TID
2008 benchmark and the FRTV Phase I validation process. Experimental results
demonstrate the effectiveness of the proposed framework to achieve a good
consistency with human visual perception. Furthermore, the best configuration
is obtained with CIELAB color space associated to KLD deviation measure.
The Poisson transform for unnormalised statistical models
Authors: Simon Barthelmé, Nicolas Chopin
Date: 2014-06-11 00:00:00

Contrary to standard statistical models, unnormalised statistical models only
specify the likelihood function up to a constant. While such models are natural
and popular, the lack of normalisation makes inference much more difficult.
Here we show that inferring the parameters of a unnormalised model on a space
$\Omega$ can be mapped onto an equivalent problem of estimating the intensity
of a Poisson point process on $\Omega$. The unnormalised statistical model now
specifies an intensity function that does not need to be normalised.
Effectively, the normalisation constant may now be inferred as just another
parameter, at no loss of information. The result can be extended to cover
non-IID models, which includes for example unnormalised models for sequences of
graphs (dynamical graphs), or for sequences of binary vectors. As a
consequence, we prove that unnormalised parameteric inference in non-IID models
can be turned into a semi-parametric estimation problem. Moreover, we show that
the noise-contrastive divergence of Gutmann & Hyv\"arinen (2012) can be
understood as an approximation of the Poisson transform, and extended to
non-IID settings. We use our results to fit spatial Markov chain models of eye
movements, where the Poisson transform allows us to turn a highly non-standard
model into vanilla semi-parametric logistic regression.
A Parallel Genetic Algorithm for Generalized Vertex Cover Problem
Authors: Drona Pratap Chandu
Date: 2014-11-27 00:00:00

This paper presents a parallel genetic algorithm for generalised vertex cover
problem (GVCP) using Hadoop Map-Reduce framework. The proposed Map-Reduce
implementation helps to run the genetic algorithm for generalized vertex cover
problem (GVCP) on multiple machines parallely and computes the solution in
relatively short time.
Graph Sensitive Indices for Comparing Clusterings
Authors: Zaeem Hussain, Marina Meila
Date: 2014-11-27 00:00:00
Tasks: Clustering

This report discusses two new indices for comparing clusterings of a set of
points. The motivation for looking at new ways for comparing clusterings stems
from the fact that the existing clustering indices are based on set cardinality
alone and do not consider the positions of data points. The new indices,
namely, the Random Walk index (RWI) and Variation of Information with Neighbors
(VIN), are both inspired by the clustering metric Variation of Information
(VI). VI possesses some interesting theoretical properties which are also
desirable in a metric for comparing clusterings. We define our indices and
discuss some of their explored properties which appear relevant for a
clustering index. We also include the results of these indices on clusterings
of some example data sets.
Interactive Visual Exploration of Topic Models using Graphs
Authors: Samuel Rönnqvist, Xiaolu Wang, Peter Sarlin
Date: 2014-09-19 00:00:00
Tasks: Descriptive, Information Retrieval, Retrieval, Topic Models

Probabilistic topic modeling is a popular and powerful family of tools for
uncovering thematic structure in large sets of unstructured text documents.
While much attention has been directed towards the modeling algorithms and
their various extensions, comparatively few studies have concerned how to
present or visualize topic models in meaningful ways. In this paper, we present
a novel design that uses graphs to visually communicate topic structure and
meaning. By connecting topic nodes via descriptive keyterms, the graph
representation reveals topic similarities, topic meaning and shared, ambiguous
keyterms. At the same time, the graph can be used for information retrieval
purposes, to find documents by topic or topic subsets. To exemplify the utility
of the design, we illustrate its use for organizing and exploring corpora of
financial patents.
Matrix Completion on Graphs
Authors: Vassilis Kalofolias, Xavier Bresson, Michael Bronstein, Pierre Vandergheynst
Date: 2014-08-07 00:00:00
Tasks: Collaborative Filtering, Matrix Completion, Missing Values, Recommendation Systems

The problem of finding the missing values of a matrix given a few of its
entries, called matrix completion, has gathered a lot of attention in the
recent years. Although the problem under the standard low rank assumption is
NP-hard, Cand\`es and Recht showed that it can be exactly relaxed if the number
of observed entries is sufficiently large. In this work, we introduce a novel
matrix completion model that makes use of proximity information about rows and
columns by assuming they form communities. This assumption makes sense in
several real-world problems like in recommender systems, where there are
communities of people sharing preferences, while products form clusters that
receive similar ratings. Our main goal is thus to find a low-rank solution that
is structured by the proximities of rows and columns encoded by graphs. We
borrow ideas from manifold learning to constrain our solution to be smooth on
these graphs, in order to implicitly force row and column proximities. Our
matrix recovery model is formulated as a convex non-smooth optimization
problem, for which a well-posed iterative scheme is provided. We study and
evaluate the proposed matrix completion on synthetic and real data, showing
that the proposed structured low-rank recovery model outperforms the standard
matrix completion model in many situations.
Scalability of using Restricted Boltzmann Machines for Combinatorial Optimization
Authors: Malte Probst, Franz Rothlauf, Jörn Grahl
Date: 2014-11-27 00:00:00
Tasks: Bayesian Optimization, Combinatorial Optimization, CPU

Estimation of Distribution Algorithms (EDAs) require flexible probability
models that can be efficiently learned and sampled. Restricted Boltzmann
Machines (RBMs) are generative neural networks with these desired properties.
We integrate an RBM into an EDA and evaluate the performance of this system in
solving combinatorial optimization problems with a single objective. We assess
how the number of fitness evaluations and the CPU time scale with problem size
and with problem complexity. The results are compared to the Bayesian
Optimization Algorithm, a state-of-the-art EDA. Although RBM-EDA requires
larger population sizes and a larger number of fitness evaluations, it
outperforms BOA in terms of CPU times, in particular if the problem is large or
complex. RBM-EDA requires less time for model building than BOA. These results
highlight the potential of using generative neural networks for combinatorial
optimization.
On the analysis of set-based fuzzy quantified reasoning using classical syllogistics
Authors: M. Pereira-Fariña, F. Díaz-Hermida, A. Bugarín
Date: 2014-11-27 00:00:00
Tasks: valid

Syllogism is a type of deductive reasoning involving quantified statements.
The syllogistic reasoning scheme in the classical Aristotelian framework
involves three crisp term sets and four linguistic quantifiers, for which the
main support is the linguistic properties of the quantifiers. A number of fuzzy
approaches for defining an approximate syllogism have been proposed for which
the main support is cardinality calculus. In this paper we analyze fuzzy
syllogistic models previously described by Zadeh and Dubois et al. and compare
their behavior with that of the classical Aristotelian framework to check which
of the 24 classical valid syllogistic reasoning patterns or moods are
particular crisp cases of these fuzzy approaches. This allows us to assess to
what extent these approaches can be considered as either plausible extensions
of the classical crisp syllogism or a basis for a general approach to the
problem of approximate syllogism.
Forecasting the Colorado River Discharge Using an Artificial Neural Network (ANN) Approach
Authors: Amirhossein Mehrkesh, Maryam Ahmadi
Date: 2014-11-27 00:00:00
Tasks: Management

Artificial Neural Network (ANN) based model is a computational approach
commonly used for modeling the complex relationships between input and output
parameters. Prediction of the flow rate of a river is a requisite for any
successful water resource management and river basin planning. In the current
survey, the effectiveness of an Artificial Neural Network was examined to
predict the Colorado River discharge. In this modeling process, an ANN model
was used to relate the discharge of the Colorado River to such parameters as
the amount of precipitation, ambient temperature and snowpack level at a
specific time of the year. The model was able to precisely study the impact of
climatic parameters on the flow rate of the Colorado River.
Unweighted Stochastic Local Search can be Effective for Random CSP Benchmarks
Authors: Christopher D. Rosin
Date: 2014-11-27 00:00:00

We present ULSA, a novel stochastic local search algorithm for random binary
constraint satisfaction problems (CSP). ULSA is many times faster than the
prior state of the art on a widely-studied suite of random CSP benchmarks.
Unlike the best previous methods for these benchmarks, ULSA is a simple
unweighted method that does not require dynamic adaptation of weights or
penalties. ULSA obtains new record best solutions satisfying 99 of 100
variables in the challenging frb100-40 benchmark instance.
The Treasure beneath Convolutional Layers: Cross-convolutional-layer Pooling for Image Classification
Authors: Lingqiao Liu, Chunhua Shen, Anton Van Den Hengel
Date: 2014-11-27 00:00:00
Proceeding: CVPR 2015 6
Tasks: General Classification, image-classification, Image Classification, Image Description

A number of recent studies have shown that a Deep Convolutional Neural
Network (DCNN) pretrained on a large dataset can be adopted as a universal
image description which leads to astounding performance in many visual
classification tasks. Most of these studies, if not all, adopt activations of
the fully-connected layer of a DCNN as the image or region representation and
it is believed that convolutional layer activations are less discriminative.
  This paper, however, advocates that if used appropriately convolutional layer
activations can be turned into a powerful image representation which enjoys
many advantages over fully-connected layer activations. This is achieved by
adopting a new technique proposed in this paper called
cross-convolutional-layer pooling. More specifically, it extracts subarrays of
feature maps of one convolutional layer as local features and pools the
extracted features with the guidance of feature maps of the successive
convolutional layer. Compared with exising methods that apply DCNNs in the
local feature setting, the proposed method is significantly faster since it
requires much fewer times of DCNN forward computation. Moreover, it avoids the
domain mismatch issue which is usually encountered when applying fully
connected layer activations to describe local regions. By applying our method
to four popular visual classification tasks, it is demonstrated that the
proposed method can achieve comparable or in some cases significantly better
performance than existing fully-connected layer based image representations
while incurring much lower computational cost.
Worst-Case Linear Discriminant Analysis as Scalable Semidefinite Feasibility Problems
Authors: Hui Li, Chunhua Shen, Anton Van Den Hengel, Qinfeng Shi
Date: 2014-11-27 00:00:00
Tasks: Dimensionality Reduction, General Classification

In this paper, we propose an efficient semidefinite programming (SDP)
approach to worst-case linear discriminant analysis (WLDA). Compared with the
traditional LDA, WLDA considers the dimensionality reduction problem from the
worst-case viewpoint, which is in general more robust for classification.
However, the original problem of WLDA is non-convex and difficult to optimize.
In this paper, we reformulate the optimization problem of WLDA into a sequence
of semidefinite feasibility problems. To efficiently solve the semidefinite
feasibility problems, we design a new scalable optimization method with
quasi-Newton methods and eigen-decomposition being the core components. The
proposed method is orders of magnitude faster than standard interior-point
based SDP solvers.
  Experiments on a variety of classification problems demonstrate that our
approach achieves better performance than standard LDA. Our method is also much
faster and more scalable than standard interior-point SDP solvers based WLDA.
The computational complexity for an SDP with $m$ constraints and matrices of
size $d$ by $d$ is roughly reduced from $\mathcal{O}(m^3+md^3+m^2d^2)$ to
$\mathcal{O}(d^3)$ ($m>d$ in our case).
Bi-objective Optimization for Robust RGB-D Visual Odometry
Authors: Tao Han, Chao Xu, Ryan Loxton, Lei Xie
Date: 2014-11-27 00:00:00
Tasks: Visual Odometry

This paper considers a new bi-objective optimization formulation for robust
RGB-D visual odometry. We investigate two methods for solving the proposed
bi-objective optimization problem: the weighted sum method (in which the
objective functions are combined into a single objective function) and the
bounded objective method (in which one of the objective functions is optimized
and the value of the other objective function is bounded via a constraint). Our
experimental results for the open source TUM RGB-D dataset show that the new
bi-objective optimization formulation is superior to several existing RGB-D
odometry methods. In particular, the new formulation yields more accurate
motion estimates and is more robust when textural or structural features in the
image sequence are lacking.
Pattern Decomposition with Complex Combinatorial Constraints: Application to Materials Discovery
Authors: Stefano Ermon, Ronan Le Bras, Santosh K. Suram, John M. Gregoire, Carla Gomes, Bart Selman, Robert B. van Dover
Date: 2014-11-27 00:00:00

Identifying important components or factors in large amounts of noisy data is
a key problem in machine learning and data mining. Motivated by a pattern
decomposition problem in materials discovery, aimed at discovering new
materials for renewable energy, e.g. for fuel and solar cells, we introduce
CombiFD, a framework for factor based pattern decomposition that allows the
incorporation of a-priori knowledge as constraints, including complex
combinatorial constraints. In addition, we propose a new pattern decomposition
algorithm, called AMIQO, based on solving a sequence of (mixed-integer)
quadratic programs. Our approach considerably outperforms the state of the art
on the materials discovery problem, scaling to larger datasets and recovering
more precise and physically meaningful decompositions. We also show the
effectiveness of our approach for enforcing background knowledge on other
application domains.
Metrics for Probabilistic Geometries
Authors: Alessandra Tosi, Søren Hauberg, Alfredo Vellido, Neil D. Lawrence
Date: 2014-11-27 00:00:00
Tasks: Dimensionality Reduction

We investigate the geometrical structure of probabilistic generative
dimensionality reduction models using the tools of Riemannian geometry. We
explicitly define a distribution over the natural metric given by the models.
We provide the necessary algorithms to compute expected metric tensors where
the distribution over mappings is given by a Gaussian process. We treat the
corresponding latent variable model as a Riemannian manifold and we use the
expectation of the metric under the Gaussian process prior to define
interpolating paths and measure distance between latent points. We show how
distances that respect the expected metric lead to more appropriate generation
of new data.
3D-Assisted Image Feature Synthesis for Novel Views of an Object
Authors: Hao Su, Fan Wang, Li Yi, Leonidas Guibas
Date: 2014-11-26 00:00:00
Tasks: Image Retrieval, Object, Retrieval

Comparing two images in a view-invariant way has been a challenging problem
in computer vision for a long time, as visual features are not stable under
large view point changes. In this paper, given a single input image of an
object, we synthesize new features for other views of the same object. To
accomplish this, we introduce an aligned set of 3D models in the same class as
the input object image. Each 3D model is represented by a set of views, and we
study the correlation of image patches between different views, seeking what we
call surrogates --- patches in one view whose feature content predicts well the
features of a patch in another view. In particular, for each patch in the novel
desired view, we seek surrogates from the observed view of the given image. For
a given surrogate, we predict that surrogate using linear combination of the
corresponding patches of the 3D model views, learn the coefficients, and then
transfer these coefficients on a per patch basis to synthesize the features of
the patch in the novel view. In this way we can create feature sets for all
views of the latent object, providing us a multi-view representation of the
object. View-invariant object comparisons are achieved simply by computing the
$L^2$ distances between the features of corresponding views. We provide
theoretical and empirical analysis of the feature synthesis process, and
evaluate the proposed view-agnostic distance (VAD) in fine-grained image
retrieval (100 object classes) and classification tasks. Experimental results
show that our synthesized features do enable view-independent comparison
between images and perform significantly better than traditional image features
in this respect.
Edge direction matrixes-based local binar patterns descriptor for shape pattern recognition
Authors: Mohammed A. Talab, Siti Norul Huda Sheikh Abdullah, Bilal Bataineh
Date: 2014-11-26 00:00:00

Shapes and texture image recognition usage is an essential branch of pattern
recognition. It is made up of techniques that aim at extracting information
from images via human knowledge and works. Local Binary Pattern (LBP) ensures
encoding global and local information and scaling invariance by introducing a
look-up table to reflect the uniformity structure of an object. However, edge
direction matrixes (EDMS) only apply global invariant descriptor which employs
first and secondary order relationships. The main idea behind this methodology
is the need of improved recognition capabilities, a goal achieved by the
combinative use of these descriptors. This collaboration aims to make use of
the major advantages each one presents, by simultaneously complementing each
other, in order to elevate their weak points. By using multiple classifier
approaches such as random forest and multi-layer perceptron neural network, the
proposed combinative descriptor are compared with the state of the art
combinative methods based on Gray-Level Co-occurrence matrix (GLCM with EDMS),
LBP and moment invariant on four benchmark dataset MPEG-7 CE-Shape-1, KTH-TIPS
image, Enghlishfnt and Arabic calligraphy . The experiments have shown the
superiority of the introduced descriptor over the GLCM with EDMS, LBP and
moment invariants and other well-known descriptor such as Scale Invariant
Feature Transform from the literature.
Understanding Deep Image Representations by Inverting Them
Authors: Aravindh Mahendran, Andrea Vedaldi
Date: 2014-11-26 00:00:00
Proceeding: CVPR 2015 6

Image representations, from SIFT and Bag of Visual Words to Convolutional
Neural Networks (CNNs), are a crucial component of almost any image
understanding system. Nevertheless, our understanding of them remains limited.
In this paper we conduct a direct analysis of the visual information contained
in representations by asking the following question: given an encoding of an
image, to which extent is it possible to reconstruct the image itself? To
answer this question we contribute a general framework to invert
representations. We show that this method can invert representations such as
HOG and SIFT more accurately than recent alternatives while being applicable to
CNNs too. We then use this technique to study the inverse of recent
state-of-the-art CNN image representations for the first time. Among our
findings, we show that several layers in CNNs retain photographically accurate
information about the image, with different degrees of geometric and
photometric invariance.
Open-source code for manifold-based 3D rotation recovery of X-ray scattering patterns
Authors: Aliakbar Jafarpour
Date: 2014-11-26 00:00:00

Single particle 3D imaging with ultrashort X-ray laser pulses is based on
collecting and combining the information content of 2D scattering patterns of
an object at different orientations. Typical sample-delivery schemes leave
little or no room for controlling the orientations. As such, the orientation
associated with a given snapshot should be estimated after the experiment. Here
we present an open-source code for the most rigorous technique having been
reported in this context. Some practical issues along with proposed solutions
are also discussed.
Heuristics for Exact Nonnegative Matrix Factorization
Authors: Arnaud Vandaele, Nicolas Gillis, François Glineur, Daniel Tuyttens
Date: 2014-11-26 00:00:00

The exact nonnegative matrix factorization (exact NMF) problem is the
following: given an $m$-by-$n$ nonnegative matrix $X$ and a factorization rank
$r$, find, if possible, an $m$-by-$r$ nonnegative matrix $W$ and an $r$-by-$n$
nonnegative matrix $H$ such that $X = WH$. In this paper, we propose two
heuristics for exact NMF, one inspired from simulated annealing and the other
from the greedy randomized adaptive search procedure. We show that these two
heuristics are able to compute exact nonnegative factorizations for several
classes of nonnegative matrices (namely, linear Euclidean distance matrices,
slack matrices, unique-disjointness matrices, and randomly generated matrices)
and as such demonstrate their superiority over standard multi-start strategies.
We also consider a hybridization between these two heuristics that allows us to
combine the advantages of both methods. Finally, we discuss the use of these
heuristics to gain insight on the behavior of the nonnegative rank, i.e., the
minimum factorization rank such that an exact NMF exists. In particular, we
disprove a conjecture on the nonnegative rank of a Kronecker product, propose a
new upper bound on the extension complexity of generic $n$-gons and conjecture
the exact value of (i) the extension complexity of regular $n$-gons and (ii)
the nonnegative rank of a submatrix of the slack matrix of the correlation
polytope.