pwc-archive/papers-with-abstracts huggingface dataset:
Approximated Robust Principal Component Analysis for Improved General Scene Background Subtraction
Authors: Salehe Erfanian Ebadi, Valia Guerra Ones, Ebroul Izquierdo
Date: 2016-03-18 00:00:00

The research reported in this paper addresses the fundamental task of
separation of locally moving or deforming image areas from a static or globally
moving background. It builds on the latest developments in the field of robust
principal component analysis, specifically, the recently reported practical
solutions for the long-standing problem of recovering the low-rank and sparse
parts of a large matrix made up of the sum of these two components. This
article addresses a few critical issues including: embedding global motion
parameters in the matrix decomposition model, i.e., estimation of global motion
parameters simultaneously with the foreground/background separation task,
considering matrix block-sparsity rather than generic matrix sparsity as
natural feature in video processing applications, attenuating background
ghosting effects when foreground is subtracted, and more critically providing
an extremely efficient algorithm to solve the low-rank/sparse matrix
decomposition task. The first aspect is important for background/foreground
separation in generic video sequences where the background usually obeys global
displacements originated by the camera motion in the capturing process. The
second aspect exploits the fact that in video processing applications the
sparse matrix has a very particular structure, where the non-zero matrix
entries are not randomly distributed but they build small blocks within the
sparse matrix. The next feature of the proposed approach addresses removal of
ghosting effects originated from foreground silhouettes and the lack of
information in the occluded background regions of the image. Finally, the
proposed model also tackles algorithmic complexity by introducing an extremely
efficient "SVD-free" technique that can be applied in most
background/foreground separation tasks for conventional video processing.
N-ary Error Correcting Coding Scheme
Authors: Joey Tianyi Zhou, Ivor W. Tsang, Shen-Shyang Ho, Klaus-Robert Muller
Date: 2016-03-18 00:00:00
Tasks: Classification, General Classification

The coding matrix design plays a fundamental role in the prediction
performance of the error correcting output codes (ECOC)-based multi-class task.
{In many-class classification problems, e.g., fine-grained categorization, it
is difficult to distinguish subtle between-class differences under existing
coding schemes due to a limited choices of coding values.} In this paper, we
investigate whether one can relax existing binary and ternary code design to
$N$-ary code design to achieve better classification performance. {In
particular, we present a novel $N$-ary coding scheme that decomposes the
original multi-class problem into simpler multi-class subproblems, which is
similar to applying a divide-and-conquer method.} The two main advantages of
such a coding scheme are as follows: (i) the ability to construct more
discriminative codes and (ii) the flexibility for the user to select the best
$N$ for ECOC-based classification. We show empirically that the optimal $N$
(based on classification performance) lies in $[3, 10]$ with some trade-off in
computational cost. Moreover, we provide theoretical insights on the dependency
of the generalization error bound of an $N$-ary ECOC on the average base
classifier generalization error and the minimum distance between any two codes
constructed. Extensive experimental results on benchmark multi-class datasets
show that the proposed coding scheme achieves superior prediction performance
over the state-of-the-art coding methods.
Comparing Time and Frequency Domain for Audio Event Recognition Using Deep Learning
Authors: Lars Hertel, Huy Phan, Alfred Mertins
Date: 2016-03-18 00:00:00

Recognizing acoustic events is an intricate problem for a machine and an
emerging field of research. Deep neural networks achieve convincing results and
are currently the state-of-the-art approach for many tasks. One advantage is
their implicit feature learning, opposite to an explicit feature extraction of
the input signal. In this work, we analyzed whether more discriminative
features can be learned from either the time-domain or the frequency-domain
representation of the audio signal. For this purpose, we trained multiple deep
networks with different architectures on the Freiburg-106 and ESC-10 datasets.
Our results show that feature learning from the frequency domain is superior to
the time domain. Moreover, additionally using convolution and pooling layers,
to explore local structures of the audio signal, significantly improves the
recognition performance and achieves state-of-the-art results.
Compression of Deep Neural Networks on the Fly
Authors: Guillaume Soulié, Vincent Gripon, Maëlys Robert
Date: 2015-09-29 00:00:00
Tasks: Object Recognition, Quantization

Thanks to their state-of-the-art performance, deep neural networks are
increasingly used for object recognition. To achieve these results, they use
millions of parameters to be trained. However, when targeting embedded
applications the size of these models becomes problematic. As a consequence,
their usage on smartphones or other resource limited devices is prohibited. In
this paper we introduce a novel compression method for deep neural networks
that is performed during the learning phase. It consists in adding an extra
regularization term to the cost function of fully-connected layers. We combine
this method with Product Quantization (PQ) of the trained weights for higher
savings in storage consumption. We evaluate our method on two data sets (MNIST
and CIFAR10), on which we achieve significantly larger compression rates than
state-of-the-art methods.
Postprocessing of Compressed Images via Sequential Denoising
Authors: Yehuda Dar, Alfred M. Bruckstein, Michael Elad, Raja Giryes
Date: 2015-10-30 00:00:00
Tasks: Denoising, Image Denoising

In this work we propose a novel postprocessing technique for
compression-artifact reduction. Our approach is based on posing this task as an
inverse problem, with a regularization that leverages on existing
state-of-the-art image denoising algorithms. We rely on the recently proposed
Plug-and-Play Prior framework, suggesting the solution of general inverse
problems via Alternating Direction Method of Multipliers (ADMM), leading to a
sequence of Gaussian denoising steps. A key feature in our scheme is a
linearization of the compression-decompression process, so as to get a
formulation that can be optimized. In addition, we supply a thorough analysis
of this linear approximation for several basic compression procedures. The
proposed method is suitable for diverse compression techniques that rely on
transform coding. Specifically, we demonstrate impressive gains in image
quality for several leading compression methods - JPEG, JPEG2000, and HEVC.
A Comparison between Deep Neural Nets and Kernel Acoustic Models for Speech Recognition
Authors: Zhiyun Lu, Dong Guo, Alireza Bagheri Garakani, Kuan Liu, Avner May, Aurelien Bellet, Linxi Fan, Michael Collins, Brian Kingsbury, Michael Picheny, Fei Sha
Date: 2016-03-18 00:00:00
Tasks: General Classification, Model Selection, speech-recognition, Speech Recognition

We study large-scale kernel methods for acoustic modeling and compare to DNNs
on performance metrics related to both acoustic modeling and recognition.
Measuring perplexity and frame-level classification accuracy, kernel-based
acoustic models are as effective as their DNN counterparts. However, on
token-error-rates DNN models can be significantly better. We have discovered
that this might be attributed to DNN's unique strength in reducing both the
perplexity and the entropy of the predicted posterior probabilities. Motivated
by our findings, we propose a new technique, entropy regularized perplexity,
for model selection. This technique can noticeably improve the recognition
performance of both types of models, and reduces the gap between them. While
effective on Broadcast News, this technique could be also applicable to other
tasks.
Unsupervised Cross-Media Hashing with Structure Preservation
Authors: Xiangyu Wang, Alex Yong-Sang Chia
Date: 2016-03-18 00:00:00
Tasks: Retrieval

Recent years have seen the exponential growth of heterogeneous multimedia
data. The need for effective and accurate data retrieval from heterogeneous
data sources has attracted much research interest in cross-media retrieval.
Here, given a query of any media type, cross-media retrieval seeks to find
relevant results of different media types from heterogeneous data sources. To
facilitate large-scale cross-media retrieval, we propose a novel unsupervised
cross-media hashing method. Our method incorporates local affinity and distance
repulsion constraints into a matrix factorization framework. Correspondingly,
the proposed method learns hash functions that generates unified hash codes
from different media types, while ensuring intrinsic geometric structure of the
data distribution is preserved. These hash codes empower the similarity between
data of different media types to be evaluated directly. Experimental results on
two large-scale multimedia datasets demonstrate the effectiveness of the
proposed method, where we outperform the state-of-the-art methods.
Learning to Navigate the Energy Landscape
Authors: Julien Valentin, Angela Dai, Matthias Nießner, Pushmeet Kohli, Philip Torr, Shahram Izadi, Cem Keskin
Date: 2016-03-18 00:00:00
Tasks: Camera Relocalization, Hand Pose Estimation, Image Retrieval, Navigate, Pose Estimation, Retrieval

In this paper, we present a novel and efficient architecture for addressing
computer vision problems that use `Analysis by Synthesis'. Analysis by
synthesis involves the minimization of the reconstruction error which is
typically a non-convex function of the latent target variables.
State-of-the-art methods adopt a hybrid scheme where discriminatively trained
predictors like Random Forests or Convolutional Neural Networks are used to
initialize local search algorithms. While these methods have been shown to
produce promising results, they often get stuck in local optima. Our method
goes beyond the conventional hybrid architecture by not only proposing multiple
accurate initial solutions but by also defining a navigational structure over
the solution space that can be used for extremely efficient gradient-free local
search. We demonstrate the efficacy of our approach on the challenging problem
of RGB Camera Relocalization. To make the RGB camera relocalization problem
particularly challenging, we introduce a new dataset of 3D environments which
are significantly larger than those found in other publicly-available datasets.
Our experiments reveal that the proposed method is able to achieve
state-of-the-art camera relocalization results. We also demonstrate the
generalizability of our approach on Hand Pose Estimation and Image Retrieval
tasks.
A Probabilistic Machine Learning Approach to Detect Industrial Plant Faults
Authors: Wei Xiao
Date: 2016-03-18 00:00:00
Tasks: BIG-bench Machine Learning, Classification, Fault Detection, General Classification, Management, regression

Fault detection in industrial plants is a hot research area as more and more
sensor data are being collected throughout the industrial process. Automatic
data-driven approaches are widely needed and seen as a promising area of
investment. This paper proposes an effective machine learning algorithm to
predict industrial plant faults based on classification methods such as
penalized logistic regression, random forest and gradient boosted tree. A
fault's start time and end time are predicted sequentially in two steps by
formulating the original prediction problems as classification problems. The
algorithms described in this paper won first place in the Prognostics and
Health Management Society 2015 Data Challenge.
From line segments to more organized Gestalts
Authors: Boshra Rajaei, Rafael Grompone von Gioi, Jean-Michel Morel
Date: 2016-03-18 00:00:00

In this paper, we reconsider the early computer vision bottom-up program,
according to which higher level features (geometric structures) in an image
could be built up recursively from elementary features by simple grouping
principles coming from Gestalt theory. Taking advantage of the (recent)
advances in reliable line segment detectors, we propose three feature detectors
that constitute one step up in this bottom up pyramid. For any digital image,
our unsupervised algorithm computes three classic Gestalts from the set of
predetected line segments: good continuations, nonlocal alignments, and bars.
The methodology is based on a common stochastic {\it a contrario model}
yielding three simple detection formulas, characterized by their number of
false alarms. This detection algorithm is illustrated on several digital
images.
Mean-Field Inference in Gaussian Restricted Boltzmann Machine
Authors: Chako Takahashi, Muneki Yasuda
Date: 2015-12-03 00:00:00

A Gaussian restricted Boltzmann machine (GRBM) is a Boltzmann machine defined
on a bipartite graph and is an extension of usual restricted Boltzmann
machines. A GRBM consists of two different layers: a visible layer composed of
continuous visible variables and a hidden layer composed of discrete hidden
variables. In this paper, we derive two different inference algorithms for
GRBMs based on the naive mean-field approximation (NMFA). One is an inference
algorithm for whole variables in a GRBM, and the other is an inference
algorithm for partial variables in a GBRBM. We compare the two methods
analytically and numerically and show that the latter method is better.
A Readability Analysis of Campaign Speeches from the 2016 US Presidential Campaign
Authors: Elliot Schumacher, Maxine Eskenazi
Date: 2016-03-18 00:00:00

Readability is defined as the reading level of the speech from grade 1 to
grade 12. It results from the use of the REAP readability analysis (vocabulary
- Collins-Thompson and Callan, 2004; syntax - Heilman et al ,2006, 2007), which
use the lexical contents and grammatical structure of the sentences in a
document to predict the reading level. After analysis, results were grouped
into the average readability of each candidate, the evolution of the
candidate's speeches' readability over time and the standard deviation, or how
much each candidate varied their speech from one venue to another. For
comparison, one speech from four past presidents and the Gettysburg Address
were also analyzed.
Predicting health inspection results from online restaurant reviews
Authors: Samantha Wong, Hamidreza Chinaei, Frank Rudzicz
Date: 2016-03-17 00:00:00
Tasks: General Classification

Informatics around public health are increasingly shifting from the
professional to the public spheres. In this work, we apply linguistic analytics
to restaurant reviews, from Yelp, in order to automatically predict official
health inspection reports. We consider two types of feature sets, i.e., keyword
detection and topic model features, and use these in several classification
methods. Our empirical analysis shows that these extracted features can predict
public health inspection reports with over 90% accuracy using simple support
vector machines.
"What happens if..." Learning to Predict the Effect of Forces in Images
Authors: Roozbeh Mottaghi, Mohammad Rastegari, Abhinav Gupta, Ali Farhadi
Date: 2016-03-17 00:00:00

What happens if one pushes a cup sitting on a table toward the edge of the
table? How about pushing a desk against a wall? In this paper, we study the
problem of understanding the movements of objects as a result of applying
external forces to them. For a given force vector applied to a specific
location in an image, our goal is to predict long-term sequential movements
caused by that force. Doing so entails reasoning about scene geometry, objects,
their attributes, and the physical rules that govern the movements of objects.
We design a deep neural network model that learns long-term sequential
dependencies of object movements while taking into account the geometry and
appearance of the scene by combining Convolutional and Recurrent Neural
Networks. Training our model requires a large-scale dataset of object movements
caused by external forces. To build a dataset of forces in scenes, we
reconstructed all images in SUN RGB-D dataset in a physics simulator to
estimate the physical movements of objects caused by external forces applied to
them. Our Forces in Scenes (ForScene) dataset contains 10,335 images in which a
variety of external forces are applied to different types of objects resulting
in more than 65,000 object movements represented in 3D. Our experimental
evaluations show that the challenging task of predicting long-term movements of
objects as their reaction to external forces is possible from a single image.
Mapping Temporal Variables into the NeuCube for Improved Pattern Recognition, Predictive Modelling and Understanding of Stream Data
Authors: Enmei Tu, Nikola Kasabov, Jie Yang
Date: 2016-03-17 00:00:00
Tasks: Prediction

This paper proposes a new method for an optimized mapping of temporal
variables, describing a temporal stream data, into the recently proposed
NeuCube spiking neural network architecture. This optimized mapping extends the
use of the NeuCube, which was initially designed for spatiotemporal brain data,
to work on arbitrary stream data and to achieve a better accuracy of temporal
pattern recognition, a better and earlier event prediction and a better
understanding of complex temporal stream data through visualization of the
NeuCube connectivity. The effect of the new mapping is demonstrated on three
bench mark problems. The first one is early prediction of patient sleep stage
event from temporal physiological data. The second one is pattern recognition
of dynamic temporal patterns of traffic in the Bay Area of California and the
last one is the Challenge 2012 contest data set. In all cases the use of the
proposed mapping leads to an improved accuracy of pattern recognition and event
prediction and a better understanding of the data when compared to traditional
machine learning techniques or spiking neural network reservoirs with arbitrary
mapping of the variables.
Deep Manifold Traversal: Changing Labels with Convolutional Features
Authors: Jacob R. Gardner, Paul Upchurch, Matt J. Kusner, Yixuan Li, Kilian Q. Weinberger, Kavita Bala, John E. Hopcroft
Date: 2015-11-19 00:00:00

Many tasks in computer vision can be cast as a "label changing" problem,
where the goal is to make a semantic change to the appearance of an image or
some subject in an image in order to alter the class membership. Although
successful task-specific methods have been developed for some label changing
applications, to date no general purpose method exists. Motivated by this we
propose deep manifold traversal, a method that addresses the problem in its
most general form: it first approximates the manifold of natural images then
morphs a test image along a traversal path away from a source class and towards
a target class while staying near the manifold throughout. The resulting
algorithm is surprisingly effective and versatile. It is completely data
driven, requiring only an example set of images from the desired source and
target domains. We demonstrate deep manifold traversal on highly diverse label
changing tasks: changing an individual's appearance (age and hair color),
changing the season of an outdoor image, and transforming a city skyline
towards nighttime.
Predicate Gradual Logic and Linguistics
Authors: Ryuta Arisaka
Date: 2016-03-17 00:00:00

There are several major proposals for treating donkey anaphora such as
discourse representation theory and the likes, or E-Type theories and the
likes. Every one of them works well for a set of specific examples that they
use to demonstrate validity of their approaches. As I show in this paper,
however, they are not very generalisable and do not account for essentially the
same problem that they remedy when it manifests in other examples. I propose
another logical approach. I develoop logic that extends a recent, propositional
gradual logic, and show that it can treat donkey anaphora generally. I also
identify and address a problem around the modern convention on existential
import. Furthermore, I show that Aristotle's syllogisms and conversion are
realisable in this logic.
Less is More: Nyström Computational Regularization
Authors: Alessandro Rudi, Raffaello Camoriano, Lorenzo Rosasco
Date: 2015-07-16 00:00:00
Proceeding: NeurIPS 2015 12

We study Nystr\"om type subsampling approaches to large scale kernel methods,
and prove learning bounds in the statistical learning setting, where random
sampling and high probability estimates are considered. In particular, we prove
that these approaches can achieve optimal learning bounds, provided the
subsampling level is suitably chosen. These results suggest a simple
incremental variant of Nystr\"om Kernel Regularized Least Squares, where the
subsampling level implements a form of computational regularization, in the
sense that it controls at the same time regularization and computations.
Extensive experimental analysis shows that the considered approach achieves
state of the art performances on benchmark large scale datasets.
Tracking multiple moving objects in images using Markov Chain Monte Carlo
Authors: Lan Jiang, Sumeetpal S. Singh
Date: 2016-03-17 00:00:00
Tasks: Image Generation

A new Bayesian state and parameter learning algorithm for multiple target
tracking (MTT) models with image observations is proposed. Specifically, a
Markov chain Monte Carlo algorithm is designed to sample from the posterior
distribution of the unknown number of targets, their birth and death times,
states and model parameters, which constitutes the complete solution to the
tracking problem. The conventional approach is to pre-process the images to
extract point observations and then perform tracking. We model the image
generation process directly to avoid potential loss of information when
extracting point observations. Numerical examples show that our algorithm has
improved tracking performance over commonly used techniques, for both synthetic
examples and real florescent microscopy data, especially in the case of dim
targets with overlapping illuminated regions.
Feature Selection with Annealing for Computer Vision and Big Data Learning
Authors: Adrian Barbu, Yiyuan She, Liangjing Ding, Gary Gramajo
Date: 2013-10-10 00:00:00
Proceeding: IEEE Transactions on Pattern Analysis and Machine Intelligence 2017 2
Tasks: feature selection, General Classification, regression

Many computer vision and medical imaging problems are faced with learning
from large-scale datasets, with millions of observations and features. In this
paper we propose a novel efficient learning scheme that tightens a sparsity
constraint by gradually removing variables based on a criterion and a schedule.
The attractive fact that the problem size keeps dropping throughout the
iterations makes it particularly suitable for big data learning. Our approach
applies generically to the optimization of any differentiable loss function,
and finds applications in regression, classification and ranking. The resultant
algorithms build variable screening into estimation and are extremely simple to
implement. We provide theoretical guarantees of convergence and selection
consistency. In addition, one dimensional piecewise linear response functions
are used to account for nonlinearity and a second order prior is imposed on
these functions to avoid overfitting. Experiments on real and synthetic data
show that the proposed method compares very well with other state of the art
methods in regression, classification and ranking while being computationally
very efficient and scalable.