pwc-archive/papers-with-abstracts huggingface dataset:
Color Image Enhancement Using the lrgb Coordinates in the Context of Support Fuzzification
Authors: Vasile Patrascu
Date: 2015-02-16 00:00:00
Tasks: Image Enhancement

Image enhancement is an important stage in the image-processing domain. The
most known image enhancement method is the histogram equalization. This method
is an automated one, and realizes a simultaneous modification for brightness
and contrast in the case of monochrome images and for brightness, contrast,
saturation and hue in the case of color images. Simple and efficient methods
can be obtained if affine transforms within logarithmic models are used. A very
important thing in the affine transform determination for color images is the
coordinate system that is used for color space representation. Thus, the using
of the RGB coordinates leads to a simultaneous modification of luminosity and
saturation. In this paper using the lrgb perceptual coordinates one can define
affine transforms, which allow a separated modification of luminosity l and
saturation s (saturation being calculated with the component rgb in the
chromatic plane). Better results can be obtained if partitions are defined on
the image support and then the pixels are separately processed in each window
belonging to the defined partition. Classical partitions frequently lead to the
appearance of some discontinuities at the boundaries between these windows. In
order to avoid all these drawbacks the classical partitions may be replaced by
fuzzy partitions. Their elements will be fuzzy windows and in each of them
there will be defined an affine transform induced by parameters using the fuzzy
mean, fuzzy variance and fuzzy saturation computed for the pixels that belong
to the analyzed window. The final image is obtained by summing up in a weight
way the images of every fuzzy window.
A Generalization of Gustafson-Kessel Algorithm Using a New Constraint Parameter
Authors: Vasile Patrascu
Date: 2015-02-16 00:00:00
Tasks: Clustering

In this paper one presents a new fuzzy clustering algorithm based on a
dissimilarity function determined by three parameters. This algorithm can be
considered a generalization of the Gustafson-Kessel algorithm for fuzzy
clustering.
Towards Building Deep Networks with Bayesian Factor Graphs
Authors: Amedeo Buonanno, Francesco A. N. Palmieri
Date: 2015-02-16 00:00:00

We propose a Multi-Layer Network based on the Bayesian framework of the
Factor Graphs in Reduced Normal Form (FGrn) applied to a two-dimensional
lattice. The Latent Variable Model (LVM) is the basic building block of a
quadtree hierarchy built on top of a bottom layer of random variables that
represent pixels of an image, a feature map, or more generally a collection of
spatially distributed discrete variables. The multi-layer architecture
implements a hierarchical data representation that, via belief propagation, can
be used for learning and inference. Typical uses are pattern completion,
correction and classification. The FGrn paradigm provides great flexibility and
modularity and appears as a promising candidate for building deep networks: the
system can be easily extended by introducing new and different (in cardinality
and in type) variables. Prior knowledge, or supervised information, can be
introduced at different scales. The FGrn paradigm provides a handy way for
building all kinds of architectures by interconnecting only three types of
units: Single Input Single Output (SISO) blocks, Sources and Replicators. The
network is designed like a circuit diagram and the belief messages flow
bidirectionally in the whole system. The learning algorithms operate only
locally within each block. The framework is demonstrated in this paper in a
three-layer structure applied to images extracted from a standard data set.
Competitive Learning with Feedforward Supervisory Signal for Pre-trained Multilayered Networks
Authors: Takashi Shinozaki, Yasushi Naruse
Date: 2013-12-20 00:00:00
Tasks: General Classification

We propose a novel learning method for multilayered neural networks which
uses feedforward supervisory signal and associates classification of a new
input with that of pre-trained input. The proposed method effectively uses rich
input information in the earlier layer for robust leaning and revising internal
representation in a multilayer neural network.
Transparallel mind: Classical computing with quantum power
Authors: Peter A. van der Helm
Date: 2014-04-08 00:00:00

Inspired by the extraordinary computing power promised by quantum computers,
the quantum mind hypothesis postulated that quantum mechanical phenomena are
the source of neuronal synchronization, which, in turn, might underlie
consciousness. Here, I present an alternative inspired by a classical computing
method with quantum power. This method relies on special distributed
representations called hyperstrings. Hyperstrings are superpositions of up to
an exponential number of strings, which -- by a single-processor classical
computer -- can be evaluated in a transparallel fashion, that is,
simultaneously as if only one string were concerned. Building on a neurally
plausible model of human visual perceptual organization, in which hyperstrings
are formal counterparts of transient neural assemblies, I postulate that
synchronization in such assemblies is a manifestation of transparallel
information processing. This accounts for the high combinatorial capacity and
speed of human visual perceptual organization and strengthens ideas that
self-organizing cognitive architecture bridges the gap between neurons and
consciousness.
Fast and Memory-Efficient Significant Pattern Mining via Permutation Testing
Authors: Felipe Llinares López, Mahito Sugiyama, Laetitia Papaxanthos, Karsten M. Borgwardt
Date: 2015-02-15 00:00:00
Tasks: Two-sample testing

We present a novel algorithm, Westfall-Young light, for detecting patterns,
such as itemsets and subgraphs, which are statistically significantly enriched
in one of two classes. Our method corrects rigorously for multiple hypothesis
testing and correlations between patterns through the Westfall-Young
permutation procedure, which empirically estimates the null distribution of
pattern frequencies in each class via permutations. In our experiments,
Westfall-Young light dramatically outperforms the current state-of-the-art
approach in terms of both runtime and memory efficiency on popular real-world
benchmark datasets for pattern mining. The key to this efficiency is that
unlike all existing methods, our algorithm neither needs to solve the
underlying frequent itemset mining problem anew for each permutation nor needs
to store the occurrence list of all frequent patterns. Westfall-Young light
opens the door to significant pattern mining on large datasets that previously
led to prohibitive runtime or memory costs.
Cost-Sensitive Support Vector Machines
Authors: Hamed Masnadi-Shirazi, Nuno Vasconcelos, Arya Iranmehr
Date: 2012-12-05 00:00:00
Tasks: General Classification

A new procedure for learning cost-sensitive SVM(CS-SVM) classifiers is
proposed. The SVM hinge loss is extended to the cost sensitive setting, and the
CS-SVM is derived as the minimizer of the associated risk. The extension of the
hinge loss draws on recent connections between risk minimization and
probability elicitation. These connections are generalized to cost-sensitive
classification, in a manner that guarantees consistency with the cost-sensitive
Bayes risk, and associated Bayes decision rule. This ensures that optimal
decision rules, under the new hinge loss, implement the Bayes-optimal
cost-sensitive classification boundary. Minimization of the new hinge loss is
shown to be a generalization of the classic SVM optimization problem, and can
be solved by identical procedures. The dual problem of CS-SVM is carefully
scrutinized by means of regularization theory and sensitivity analysis and the
CS-SVM algorithm is substantiated. The proposed algorithm is also extended to
cost-sensitive learning with example dependent costs. The minimum cost
sensitive risk is proposed as the performance measure and is connected to ROC
analysis through vector optimization. The resulting algorithm avoids the
shortcomings of previous approaches to cost-sensitive SVM design, and is shown
to have superior experimental performance on a large number of cost sensitive
and imbalanced datasets.
Accurate automatic segmentation of retina layers with emphasis on first layer
Authors: Mahdi Salarian
Date: 2015-01-25 00:00:00

Quantification of intra-retinal boundaries in optical coherence tomography
(OCT) is a crucial task for studying and diagnosing neurological and ocular
diseases. Since manual segmentation of layers is usually a time consuming task
and relay on user, a lot of attempts done to do it automatically and without
interference of user. Although for extracting all layers usually same procedure
is applied but finding the first layer is usually more difficult due to
vanishing it in some region specially close to Fobia. To have a general
software, beside using common methods like applying shortest path algorithm on
global gradient of image, some extra steps are used here to confine search area
for Dijstra algorithm especially for the second layer. Results demonstrates
high accuracy in segmenting all present layers, especially the first one that
is important for diagnosing issue.
segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection
Authors: Yukun Zhu, Raquel Urtasun, Ruslan Salakhutdinov, Sanja Fidler
Date: 2015-02-15 00:00:00
Proceeding: CVPR 2015 6
Tasks: Object, object-detection, Object Detection, Segmentation, Semantic Segmentation

In this paper, we propose an approach that exploits object segmentation in
order to improve the accuracy of object detection. We frame the problem as
inference in a Markov Random Field, in which each detection hypothesis scores
object appearance as well as contextual information using Convolutional Neural
Networks, and allows the hypothesis to choose and score a segment out of a
large pool of accurate object segmentation proposals. This enables the detector
to incorporate additional evidence when it is available and thus results in
more accurate detections. Our experiments show an improvement of 4.1% in mAP
over the R-CNN baseline on PASCAL VOC 2010, and 3.4% over the current
state-of-the-art, demonstrating the power of our approach.
Spatial Stimuli Gradient Sketch Model
Authors: Joshin John Mathew, Alex Pappachen James
Date: 2015-02-15 00:00:00
Tasks: Edge Detection, Face Recognition, model, Object

The inability of automated edge detection methods inspired from primal sketch
models to accurately calculate object edges under the influence of pixel noise
is an open problem. Extending the principles of image perception i.e.
Weber-Fechner law, and Sheperd similarity law, we propose a new edge detection
method and formulation that use perceived brightness and neighbourhood
similarity calculations in the determination of robust object edges. The
robustness of the detected edges is benchmark against Sobel, SIS, Kirsch, and
Prewitt edge detection methods in an example face recognition problem showing
statistically significant improvement in recognition accuracy and pixel noise
tolerance.
Constrained Nonlinear Model Predictive Control of an MMA Polymerization Process via Evolutionary Optimization
Authors: Masoud Abbaszadeh, Reza Solgi
Date: 2015-02-15 00:00:00
Tasks: Model Predictive Control

In this work, a nonlinear model predictive controller is developed for a
batch polymerization process. The physical model of the process is
parameterized along a desired trajectory resulting in a trajectory linearized
piecewise model (a multiple linear model bank) and the parameters are
identified for an experimental polymerization reactor. Then, a multiple model
adaptive predictive controller is designed for thermal trajectory tracking of
the MMA polymerization. The input control signal to the process is constrained
by the maximum thermal power provided by the heaters. The constrained
optimization in the model predictive controller is solved via genetic
algorithms to minimize a DMC cost function in each sampling interval.
Cardiac MR Image Segmentation Techniques: an overview
Authors: Tizita Nesibu Shewaye
Date: 2015-02-14 00:00:00
Tasks: Cardiac Segmentation, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Broadly speaking, the objective in cardiac image segmentation is to delineate
the outer and inner walls of the heart to segment out either the entire or
parts of the organ boundaries. This paper will focus on MR images as they are
the most widely used in cardiac segmentation -- as a result of the accurate
morphological information and better soft tissue contrast they provide. This
cardiac segmentation information is very useful as it eases physical
measurements that provides useful metrics for cardiac diagnosis such as
infracted volumes, ventricular volumes, ejection fraction, myocardial mass,
cardiac movement, and the like. But, this task is difficult due to the
intensity and texture similarities amongst the different cardiac and background
structures on top of some noisy artifacts present in MR images. Thus far,
various researchers have proposed different techniques to solve some of the
pressing issues. This seminar paper presents an overview of representative
medical image segmentation techniques. The paper also highlights preferred
approaches for segmentation of the four cardiac chambers: the left ventricle
(LV), right ventricle (RV), left atrium (LA) and right atrium (RA), on short
axis image planes.
Asymptotic Justification of Bandlimited Interpolation of Graph signals for Semi-Supervised Learning
Authors: Aamir Anis, Aly El Gamal, A. Salman Avestimehr, Antonio Ortega
Date: 2015-02-14 00:00:00

Graph-based methods play an important role in unsupervised and
semi-supervised learning tasks by taking into account the underlying geometry
of the data set. In this paper, we consider a statistical setting for
semi-supervised learning and provide a formal justification of the recently
introduced framework of bandlimited interpolation of graph signals. Our
analysis leads to the interpretation that, given enough labeled data, this
method is very closely related to a constrained low density separation problem
as the number of data points tends to infinity. We demonstrate the practical
utility of our results through simple experiments.
Analysis and Understanding of Various Models for Efficient Representation and Accurate Recognition of Human Faces
Authors: Dharini S., Guru Prasad M., Hari haran. V., Kiran Tej J. L., Kunal Ghosh
Date: 2013-12-13 00:00:00
Tasks: Face Recognition

In this paper we have tried to compare the various face recognition models against their classical problems. We look at the methods followed by these approaches and evaluate to what extent they are able to solve the problems. All methods proposed have some drawbacks under certain conditions. To overcome these drawbacks we propose a multi-model approach
Probabilistic Models for High-Order Projective Dependency Parsing
Authors: Xuezhe Ma, Hai Zhao
Date: 2015-02-14 00:00:00
Tasks: Dependency Parsing, parameter estimation, Vocal Bursts Intensity Prediction

This paper presents generalized probabilistic models for high-order
projective dependency parsing and an algorithmic framework for learning these
statistical models involving dependency trees. Partition functions and
marginals for high-order dependency trees can be computed efficiently, by
adapting our algorithms which extend the inside-outside algorithm to
higher-order cases. To show the effectiveness of our algorithms, we perform
experiments on three languages---English, Chinese and Czech, using maximum
conditional likelihood estimation for model training and L-BFGS for parameter
estimation. Our methods achieve competitive performance for English, and
outperform all previously reported dependency parsers for Chinese and Czech.
A Distributional Representation Model For Collaborative Filtering
Authors: Zhang Junlin, Cai Heng, Huang Tongwen, Xue Huiping
Date: 2015-02-14 00:00:00
Tasks: Collaborative Filtering, model

In this paper, we propose a very concise deep learning approach for
collaborative filtering that jointly models distributional representation for
users and items. The proposed framework obtains better performance when
compared against current state-of-art algorithms and that made the
distributional representation model a promising direction for further research
in the collaborative filtering.
Geometry-based Adaptive Symbolic Approximation for Fast Sequence Matching on Manifolds
Authors: Rushil Anirudh, Pavan Turaga
Date: 2014-03-04 00:00:00
Tasks: Activity Recognition, Dynamic Texture Recognition

In this paper, we consider the problem of fast and efficient indexing
techniques for sequences evolving in non-Euclidean spaces. This problem has
several applications in the areas of human activity analysis, where there is a
need to perform fast search, and recognition in very high dimensional spaces.
The problem is made more challenging when representations such as landmarks,
contours, and human skeletons etc. are naturally studied in a non-Euclidean
setting where even simple operations are much more computationally intensive
than their Euclidean counterparts. We propose a geometry and data adaptive
symbolic framework that is shown to enable the deployment of fast and accurate
algorithms for activity recognition, dynamic texture recognition, motif
discovery. Toward this end, we present generalizations of key concepts of
piece-wise aggregation and symbolic approximation for the case of non-Euclidean
manifolds. We show that one can replace expensive geodesic computations with
much faster symbolic computations with little loss of accuracy in activity
recognition and discovery applications. The framework is general enough to work
across both Euclidean and non-Euclidean spaces, depending on appropriate
feature representations without compromising on the ultra-low bandwidth, high
speed and high accuracy. The proposed methods are ideally suited for real-time
systems and low complexity scenarios.
ggRandomForests: Visually Exploring a Random Forest for Regression
Authors: John Ehrlinger
Date: 2015-01-28 00:00:00
Tasks: regression

Random Forests [Breiman:2001] (RF) are a fully non-parametric statistical
method requiring no distributional assumptions on covariate relation to the
response. RF are a robust, nonlinear technique that optimizes predictive
accuracy by fitting an ensemble of trees to stabilize model estimates. The
randomForestSRC package (http://cran.r-project.org/package=randomForestSRC) is
a unified treatment of Breiman's random forests for survival, regression and
classification problems. Predictive accuracy make RF an attractive alternative
to parametric models, though complexity and interpretability of the forest
hinder wider application of the method. We introduce the ggRandomForests
package (http://cran.r-project.org/package=ggRandomForests), for visually
understand random forest models grown in R with the randomForestSRC package.
  The vignette is a tutorial for using the ggRandomForests package with the
randomForestSRC package for building and post-processing a regression random
forest. In this tutorial, we explore a random forest model for the Boston
Housing Data, available in the MASS package. We grow a random forest for
regression and demonstrate how ggRandomForests can be used when determining
variable associations, interactions and how the response depends on predictive
variables within the model. The tutorial demonstrates the design and usage of
many of ggRandomForests functions and features how to modify and customize the
resulting ggplot2 graphic objects along the way.
  A development version of the ggRandomForests package is available on Github.
We invite comments, feature requests and bug reports for this package at
(https://github.com/ehrlinger/ggRandomForests).
Non-Adaptive Learning a Hidden Hipergraph
Authors: Hasan Abasi, Nader H. Bshouty, Hanna Mazzawi
Date: 2015-02-13 00:00:00

We give a new deterministic algorithm that non-adaptively learns a hidden
hypergraph from edge-detecting queries. All previous non-adaptive algorithms
either run in exponential time or have non-optimal query complexity. We give
the first polynomial time non-adaptive learning algorithm for learning
hypergraph that asks almost optimal number of queries.
Long-short Term Motion Feature for Action Classification and Retrieval
Authors: Zhenzhong Lan, Xuanchong Li, Ming Lin, Alexander G. Hauptmann
Date: 2015-02-13 00:00:00
Tasks: Action Classification, Classification, General Classification, Retrieval, Video Classification

We propose a method for representing motion information for video
classification and retrieval. We improve upon local descriptor based methods
that have been among the most popular and successful models for representing
videos. The desired local descriptors need to satisfy two requirements: 1) to
be representative, 2) to be discriminative. Therefore, they need to occur
frequently enough in the videos and to be be able to tell the difference among
different types of motions. To generate such local descriptors, the video
blocks they are based on must contain just the right amount of motion
information. However, current state-of-the-art local descriptor methods use
video blocks with a single fixed size, which is insufficient for covering
actions with varying speeds. In this paper, we introduce a long-short term
motion feature that generates descriptors from video blocks with multiple
lengths, thus covering motions with large speed variance. Experimental results
show that, albeit simple, our model achieves state-of-the-arts results on
several benchmark datasets.
Bayesian Models of Graphs, Arrays and Other Exchangeable Random Structures
Authors: Peter Orbanz, Daniel M. Roy
Date: 2013-12-30 00:00:00
Tasks: Clustering, Collaborative Filtering, Link Prediction

The natural habitat of most Bayesian methods is data represented by
exchangeable sequences of observations, for which de Finetti's theorem provides
the theoretical foundation. Dirichlet process clustering, Gaussian process
regression, and many other parametric and nonparametric Bayesian models fall
within the remit of this framework; many problems arising in modern data
analysis do not. This article provides an introduction to Bayesian models of
graphs, matrices, and other data that can be modeled by random structures. We
describe results in probability theory that generalize de Finetti's theorem to
such data and discuss their relevance to nonparametric Bayesian modeling. With
the basic ideas in place, we survey example models available in the literature;
applications of such models include collaborative filtering, link prediction,
and graph and network analysis. We also highlight connections to recent
developments in graph theory and probability, and sketch the more general
mathematical foundation of Bayesian methods for other types of data beyond
sequences and arrays.