pwc-archive/papers-with-abstracts huggingface dataset:
Significance of the levels of spectral valleys with application to front/back distinction of vowel sounds
Authors: T. V. Ananthapadmanabha, A. G. Ramakrishnan, Shubham Sharma
Date: 2015-06-16 00:00:00

An objective critical distance (OCD) has been defined as that spacing between
adjacent formants, when the level of the valley between them reaches the mean
spectral level. The measured OCD lies in the same range (viz., 3-3.5 bark) as
the critical distance determined by subjective experiments for similar
experimental conditions. The level of spectral valley serves a purpose similar
to that of the spacing between the formants with an added advantage that it can
be measured from the spectral envelope without an explicit knowledge of formant
frequencies. Based on the relative spacing of formant frequencies, the level of
the spectral valley, VI (between F1 and F2) is much higher than the level of
VII (spectral valley between F2 and F3) for back vowels and vice-versa for
front vowels. Classification of vowels into front/back distinction with the
difference (VI-VII) as an acoustic feature, tested using TIMIT, NTIMIT, Tamil
and Kannada language databases gives, on the average, an accuracy of about 95%,
which is comparable to the accuracy (90.6%) obtained using a neural network
classifier trained and tested using MFCC as the feature vector for TIMIT
database. The acoustic feature (VI-VII) has also been tested for its robustness
on the TIMIT database for additive white and babble noise and an accuracy of
about 95% has been obtained for SNRs down to 25 dB for both types of noise.
Nonlinear Spectral Analysis via One-homogeneous Functionals - Overview and Future Prospects
Authors: Guy Gilboa, Michael Moeller, Martin Burger
Date: 2015-10-05 00:00:00

We present in this paper the motivation and theory of nonlinear spectral
representations, based on convex regularizing functionals. Some comparisons and
analogies are drawn to the fields of signal processing, harmonic analysis and
sparse representations. The basic approach, main results and initial
applications are shown. A discussion of open problems and future directions
concludes this work.
Boosting in the presence of outliers: adaptive classification with non-convex loss functions
Authors: Alexander Hanbo Li, Jelena Bradic
Date: 2015-10-05 00:00:00
Tasks: Binary Classification, General Classification

This paper examines the role and efficiency of the non-convex loss functions
for binary classification problems. In particular, we investigate how to design
a simple and effective boosting algorithm that is robust to the outliers in the
data. The analysis of the role of a particular non-convex loss for prediction
accuracy varies depending on the diminishing tail properties of the gradient of
the loss -- the ability of the loss to efficiently adapt to the outlying data,
the local convex properties of the loss and the proportion of the contaminated
data. In order to use these properties efficiently, we propose a new family of
non-convex losses named $\gamma$-robust losses. Moreover, we present a new
boosting framework, {\it Arch Boost}, designed for augmenting the existing work
such that its corresponding classification algorithm is significantly more
adaptable to the unknown data contamination. Along with the Arch Boosting
framework, the non-convex losses lead to the new class of boosting algorithms,
named adaptive, robust, boosting (ARB). Furthermore, we present theoretical
examples that demonstrate the robustness properties of the proposed algorithms.
In particular, we develop a new breakdown point analysis and a new influence
function analysis that demonstrate gains in robustness. Moreover, we present
new theoretical results, based only on local curvatures, which may be used to
establish statistical and optimization properties of the proposed Arch boosting
algorithms with highly non-convex loss functions. Extensive numerical
calculations are used to illustrate these theoretical properties and reveal
advantages over the existing boosting methods when data exhibits a number of
outliers.
Stochastic Texture Difference for Scale-Dependent Data Analysis
Authors: Nicolas Brodu, Hussein Yahia
Date: 2015-03-11 00:00:00

This article introduces the Stochastic Texture Difference method for
analyzing data at prescribed spatial and value scales. This method relies on
constrained random walks around each pixel, describing how nearby image values
typically evolve on each side of this pixel. Textures are represented as
probability distributions of such random walks, so a texture difference
operator is statistically defined as a distance between these distributions in
a suitable reproducing kernel Hilbert space. The method is thus not limited to
scalar pixel values: any data type for which a kernel is available may be
considered, from color triplets and multispectral vector data to strings,
graphs, and more. By adjusting the size of the neighborhoods that are compared,
the method is implicitly scale-dependent. It is also able to focus on either
small changes or large gradients. We demonstrate how it can be used to infer
spatial and data value characteristic scales in measured signals and natural
images.
Automatic Taxonomy Extraction from Query Logs with no Additional Sources of Information
Authors: Miguel Fernandez-Fernandez, Daniel Gayo-Avello
Date: 2015-10-02 00:00:00

Search engine logs store detailed information on Web users interactions.
Thus, as more and more people use search engines on a daily basis, important
trails of users common knowledge are being recorded in those files. Previous
research has shown that it is possible to extract concept taxonomies from full
text documents, while other scholars have proposed methods to obtain similar
queries from query logs. We propose a mixture of both lines of research, that
is, mining query logs not to find related queries nor query hierarchies, but
actual term taxonomies that could be used to improve search engine
effectiveness and efficiency. As a result, in this study we have developed a
method that combines lexical heuristics with a supervised classification model
to successfully extract hyponymy relations from specialization search patterns
revealed from log missions, with no additional sources of information, and in a
language independent way.
GPU-Based Computation of 2D Least Median of Squares with Applications to Fast and Robust Line Detection
Authors: Gil Shapira, Tal Hassner
Date: 2015-10-05 00:00:00
Tasks: CPU, GPU, Line Detection

The 2D Least Median of Squares (LMS) is a popular tool in robust regression
because of its high breakdown point: up to half of the input data can be
contaminated with outliers without affecting the accuracy of the LMS estimator.
The complexity of 2D LMS estimation has been shown to be $\Omega(n^2)$ where
$n$ is the total number of points. This high theoretical complexity along with
the availability of graphics processing units (GPU) motivates the development
of a fast, parallel, GPU-based algorithm for LMS computation. We present a CUDA
based algorithm for LMS computation and show it to be much faster than the
optimal state of the art single threaded CPU algorithm. We begin by describing
the proposed method and analyzing its performance. We then demonstrate how it
can be used to modify the well-known Hough Transform (HT) in order to
efficiently detect image lines in noisy images. Our method is compared with
standard HT-based line detection methods and shown to overcome their
shortcomings in terms of both efficiency and accuracy.
Relaxed Multiple-Instance SVM with Application to Object Discovery
Authors: Xinggang Wang, Zhuotun Zhu, Cong Yao, Xiang Bai
Date: 2015-10-05 00:00:00
Proceeding: ICCV 2015 12
Tasks: General Classification, image-classification, Image Classification, Multiple Instance Learning, Object, object-detection, Object Detection, Object Discovery, Visual Tracking

Multiple-instance learning (MIL) has served as an important tool for a wide
range of vision applications, for instance, image classification, object
detection, and visual tracking. In this paper, we propose a novel method to
solve the classical MIL problem, named relaxed multiple-instance SVM (RMI-SVM).
We treat the positiveness of instance as a continuous variable, use Noisy-OR
model to enforce the MIL constraints, and jointly optimize the bag label and
instance label in a unified framework. The optimization problem can be
efficiently solved using stochastic gradient decent. The extensive experiments
demonstrate that RMI-SVM consistently achieves superior performance on various
benchmarks for MIL. Moreover, we simply applied RMI-SVM to a challenging vision
task, common object discovery. The state-of-the-art results of object discovery
on Pascal VOC datasets further confirm the advantages of the proposed method.
Calculating entropy at different scales among diverse communication systems
Authors: Gerardo Febres, Klaus Jaffe
Date: 2015-10-05 00:00:00

We evaluated the impact of changing the observation scale over the entropy
measures for text descriptions. MIDI coded Music, computer code and two human
natural languages were studied at the scale of characters, words, and at the
Fundamental Scale resulting from adjusting the symbols length used to interpret
each text-description until it produced minimum entropy. The results show that
the Fundamental Scale method is comparable with the use of words when measuring
entropy levels in written texts. However, this method can also be used in
communication systems lacking words such as music. Measuring symbolic entropy
at the fundamental scale allows to calculate quantitatively, relative levels of
complexity for different communication systems. The results open novel vision
on differences among the structure of the communication systems studied.
Quadratic Optimization with Orthogonality Constraints: Explicit Lojasiewicz Exponent and Linear Convergence of Line-Search Methods
Authors: Huikang Liu, Weijie Wu, Anthony Man-Cho So
Date: 2015-10-05 00:00:00

A fundamental class of matrix optimization problems that arise in many areas
of science and engineering is that of quadratic optimization with orthogonality
constraints. Such problems can be solved using line-search methods on the
Stiefel manifold, which are known to converge globally under mild conditions.
To determine the convergence rate of these methods, we give an explicit
estimate of the exponent in a Lojasiewicz inequality for the (non-convex) set
of critical points of the aforementioned class of problems. By combining such
an estimate with known arguments, we are able to establish the linear
convergence of a large class of line-search methods. A key step in our proof is
to establish a local error bound for the set of critical points, which may be
of independent interest.
Single Image Dehazing through Improved Atmospheric Light Estimation
Authors: Huimin Lu, Yujie Li, Shota Nakashima, Seiichi Serikawa
Date: 2015-10-05 00:00:00
Tasks: Image Dehazing, Single Image Dehazing

Image contrast enhancement for outdoor vision is important for smart car
auxiliary transport systems. The video frames captured in poor weather
conditions are often characterized by poor visibility. Most image dehazing
algorithms consider to use a hard threshold assumptions or user input to
estimate atmospheric light. However, the brightest pixels sometimes are objects
such as car lights or streetlights, especially for smart car auxiliary
transport systems. Simply using a hard threshold may cause a wrong estimation.
In this paper, we propose a single optimized image dehazing method that
estimates atmospheric light efficiently and removes haze through the estimation
of a semi-globally adaptive filter. The enhanced images are characterized with
little noise and good exposure in dark regions. The textures and edges of the
processed images are also enhanced significantly.
Automatic 3D Liver Segmentation Using Sparse Representation of Global and Local Image Information via Level Set Formulation
Authors: Saif Dawood Salman Al-Shaikhli, Michael Ying Yang, Bodo Rosenhahn
Date: 2015-08-06 00:00:00
Tasks: Liver Segmentation, Segmentation

In this paper, a novel framework for automated liver segmentation via a level
set formulation is presented. A sparse representation of both global
(region-based) and local (voxel-wise) image information is embedded in a level
set formulation to innovate a new cost function. Two dictionaries are build: A
region-based feature dictionary and a voxel-wise dictionary. These dictionaries
are learned, using the K-SVD method, from a public database of liver
segmentation challenge (MICCAI-SLiver07). The learned dictionaries provide
prior knowledge to the level set formulation. For the quantitative evaluation,
the proposed method is evaluated using the testing data of MICCAI-SLiver07
database. The results are evaluated using different metric scores computed by
the challenge organizers. The experimental results demonstrate the superiority
of the proposed framework by achieving the highest segmentation accuracy
(79.6\%) in comparison to the state-of-the-art methods.
Reasoning in complex environments with the SelectScript declarative language
Authors: André Dietrich, Sebastian Zug, Luigi Nardi, Jörg Kaiser
Date: 2015-08-17 00:00:00
Tasks: Information Retrieval, Retrieval

SelectScript is an extendable, adaptable, and declarative domain-specific
language aimed at information retrieval from simulation environments and
robotic world models in an SQL-like manner. In this work we have extended the
language in two directions. First, we have implemented hierarchical queries;
second, we improve efficiency enabling manual design space exploration on
different "search" strategies. We demonstrate the applicability of such
extensions in two application problems; the basic language concepts are
explained by solving the classical problem of the Towers of Hanoi and then a
common path planning problem in a complex 3D environment is implemented.
Convex Modeling of Interactions with Strong Heredity
Authors: Asad Haris, Daniela Witten, Noah Simon
Date: 2014-10-13 00:00:00

We consider the task of fitting a regression model involving interactions
among a potentially large set of covariates, in which we wish to enforce strong
heredity. We propose FAMILY, a very general framework for this task. Our
proposal is a generalization of several existing methods, such as VANISH
[Radchenko and James, 2010], hierNet [Bien et al., 2013], the all-pairs lasso,
and the lasso using only main effects. It can be formulated as the solution to
a convex optimization problem, which we solve using an efficient alternating
directions method of multipliers (ADMM) algorithm. This algorithm has
guaranteed convergence to the global optimum, can be easily specialized to any
convex penalty function of interest, and allows for a straightforward extension
to the setting of generalized linear models. We derive an unbiased estimator of
the degrees of freedom of FAMILY, and explore its performance in a simulation
study and on an HIV sequence data set.
Holistically-Nested Edge Detection
Authors: Saining Xie, Zhuowen Tu
Date: 2015-04-24 00:00:00
Proceeding: ICCV 2015 12
Tasks: Boundary Detection, Edge Detection

We develop a new edge detection algorithm that tackles two important issues
in this long-standing vision problem: (1) holistic image training and
prediction; and (2) multi-scale and multi-level feature learning. Our proposed
method, holistically-nested edge detection (HED), performs image-to-image
prediction by means of a deep learning model that leverages fully convolutional
neural networks and deeply-supervised nets. HED automatically learns rich
hierarchical representations (guided by deep supervision on side responses)
that are important in order to approach the human ability resolve the
challenging ambiguity in edge and object boundary detection. We significantly
advance the state-of-the-art on the BSD500 dataset (ODS F-score of .782) and
the NYU Depth dataset (ODS F-score of .746), and do so with an improved speed
(0.4 second per image) that is orders of magnitude faster than some recent
CNN-based edge detection algorithms.
A Lower Bound for the Optimization of Finite Sums
Authors: Alekh Agarwal, Leon Bottou
Date: 2014-10-02 00:00:00

This paper presents a lower bound for optimizing a finite sum of $n$
functions, where each function is $L$-smooth and the sum is $\mu$-strongly
convex. We show that no algorithm can reach an error $\epsilon$ in minimizing
all functions from this class in fewer than $\Omega(n +
\sqrt{n(\kappa-1)}\log(1/\epsilon))$ iterations, where $\kappa=L/\mu$ is a
surrogate condition number. We then compare this lower bound to upper bounds
for recently developed methods specializing to this setting. When the functions
involved in this sum are not arbitrary, but based on i.i.d. random data, then
we further contrast these complexity results with those for optimal first-order
methods to directly optimize the sum. The conclusion we draw is that a lot of
caution is necessary for an accurate comparison, and identify machine learning
scenarios where the new methods help computationally.
Background Image Generation Using Boolean Operations
Authors: Kardi Teknomo, Proceso Fernandez
Date: 2015-10-04 00:00:00
Tasks: Image Generation, object-detection, Object Detection

Tracking moving objects from a video sequence requires segmentation of these
objects from the background image. However, getting the actual background image
automatically without object detection and using only the video is difficult.
In this paper, we describe a novel algorithm that generates background from
real world images without foreground detection. The algorithm assumes that the
background image is shown in the majority of the video. Given this simple
assumption, the method described in this paper is able to accurately generate,
with high probability, the background image from a video using only a small
number of binary operations.
Approximate Fisher Kernels of non-iid Image Models for Image Categorization
Authors: Ramazan Gokberk Cinbis, Jakob Verbeek, Cordelia Schmid
Date: 2015-10-03 00:00:00
Tasks: Image Categorization

The bag-of-words (BoW) model treats images as sets of local descriptors and
represents them by visual word histograms. The Fisher vector (FV)
representation extends BoW, by considering the first and second order
statistics of local descriptors. In both representations local descriptors are
assumed to be identically and independently distributed (iid), which is a poor
assumption from a modeling perspective. It has been experimentally observed
that the performance of BoW and FV representations can be improved by employing
discounting transformations such as power normalization. In this paper, we
introduce non-iid models by treating the model parameters as latent variables
which are integrated out, rendering all local regions dependent. Using the
Fisher kernel principle we encode an image by the gradient of the data
log-likelihood w.r.t. the model hyper-parameters. Our models naturally generate
discounting effects in the representations; suggesting that such
transformations have proven successful because they closely correspond to the
representations obtained for non-iid models. To enable tractable computation,
we rely on variational free-energy bounds to learn the hyper-parameters and to
compute approximate Fisher kernels. Our experimental evaluation results
validate that our models lead to performance improvements comparable to using
power normalization, as employed in state-of-the-art feature aggregation
methods.
Projected Iterative Soft-thresholding Algorithm for Tight Frames in Compressed Sensing Magnetic Resonance Imaging
Authors: Yunsong Liu, Zhifang Zhan, Jian-Feng Cai, Di Guo, Zhong Chen, Xiaobo Qu
Date: 2015-04-29 00:00:00
Tasks: compressed sensing, Image Reconstruction

Compressed sensing has shown great potentials in accelerating magnetic
resonance imaging. Fast image reconstruction and high image quality are two
main issues faced by this new technology. It has been shown that, redundant
image representations, e.g. tight frames, can significantly improve the image
quality. But how to efficiently solve the reconstruction problem with these
redundant representation systems is still challenging. This paper attempts to
address the problem of applying iterative soft-thresholding algorithm (ISTA) to
tight frames based magnetic resonance image reconstruction. By introducing the
canonical dual frame to construct the orthogonal projection operator on the
range of the analysis sparsity operator, we propose a projected iterative
soft-thresholding algorithm (pISTA) and further accelerate it by incorporating
the strategy proposed by Beck and Teboulle in 2009. We theoretically prove that
pISTA converges to the minimum of a function with a balanced tight frame
sparsity. Experimental results demonstrate that the proposed algorithm achieves
better reconstruction than the widely used synthesis sparse model and the
accelerated pISTA converges faster or comparable to the state-of-art smoothing
FISTA. One major advantage of pISTA is that only one extra parameter, the step
size, is introduced and the numerical solution is stable to it in terms of
image reconstruction errors, thus allowing easily setting in many fast magnetic
resonance imaging applications.
Intelligent Search Optimization using Artificial Fuzzy Logics
Authors: Jai Manral
Date: 2015-10-03 00:00:00
Tasks: TAG

Information on the web is prodigious; searching relevant information is
difficult making web users to rely on search engines for finding relevant
information on the web. Search engines index and categorize web pages according
to their contents using crawlers and rank them accordingly. For given user
query they retrieve millions of webpages and display them to users according to
web-page rank. Every search engine has their own algorithms based on certain
parameters for ranking web-pages. Search Engine Optimization (SEO) is that
technique by which webmasters try to improve ranking of their websites by
optimizing it according to search engines ranking parameters. It is the aim of
this research to identify the most popular SEO techniques used by search
engines for ranking web-pages and to establish their importance for indexing
and categorizing web data. The research tries to establish that using more SEO
parameters in ranking algorithms helps in retrieving better search results thus
increasing user satisfaction.
  In the accomplished research, a web based Meta search engine is proposed to
aggregates search results from different search engines and rank web-pages
based on new page ranking algorithm which will assign heuristic page rank to
web-pages based on SEO parameters such as title tag, Meta description, sitemap
etc. The research also provides insight into techniques which webmasters can
use for better ranking their websites in Google and Bing.
  Initial results has shown that using certain SEO parameters in present
ranking algorithm helps in retrieving more useful results for user queries.
These results generated from Meta search engine outperformed existing search
engines in terms of better retrieved search results and high precision.