pwc-archive/papers-with-abstracts huggingface dataset:
A Real Time Facial Expression Classification System Using Local Binary Patterns
Authors: S. L. Happy, Anjith George, Aurobinda Routray
Date: 2015-05-15 00:00:00
Tasks: Emotion Recognition, Face Detection, General Classification

Facial expression analysis is one of the popular fields of research in human
computer interaction (HCI). It has several applications in next generation user
interfaces, human emotion analysis, behavior and cognitive modeling. In this
paper, a facial expression classification algorithm is proposed which uses Haar
classifier for face detection purpose, Local Binary Patterns (LBP) histogram of
different block sizes of a face image as feature vectors and classifies various
facial expressions using Principal Component Analysis (PCA). The algorithm is
implemented in real time for expression classification since the computational
complexity of the algorithm is small. A customizable approach is proposed for
facial expression analysis, since the various expressions and intensity of
expressions vary from person to person. The system uses grayscale frontal face
images of a person to classify six basic emotions namely happiness, sadness,
disgust, fear, surprise and anger.
A Video Database of Human Faces under Near Infra-Red Illumination for Human Computer Interaction Aplications
Authors: S. L. Happy, Anirban Dasgupta, Anjith George, Aurobinda Routray
Date: 2015-05-15 00:00:00
Tasks: Face Detection, Gaze Estimation, Pose Estimation

Human Computer Interaction (HCI) is an evolving area of research for coherent
communication between computers and human beings. Some of the important
applications of HCI as reported in literature are face detection, face pose
estimation, face tracking and eye gaze estimation. Development of algorithms
for these applications is an active field of research. However, availability of
standard database to validate such algorithms is insufficient. This paper
discusses the creation of such a database created under Near Infra-Red (NIR)
illumination. NIR illumination has gained its popularity for night mode
applications since prolonged exposure to Infra-Red (IR) lighting may lead to
many health issues. The database contains NIR videos of 60 subjects in
different head orientations and with different facial expressions, facial
occlusions and illumination variation. This new database can be a very valuable
resource for development and evaluation of algorithms on face detection, eye
detection, head tracking, eye gaze tracking etc. in NIR lighting.
Robust Facial Expression Classification Using Shape and Appearance Features
Authors: S. L. Happy, Aurobinda Routray
Date: 2015-05-15 00:00:00
Tasks: Classification, Facial Expression Recognition, Facial Expression Recognition (FER), General Classification

Facial expression recognition has many potential applications which has
attracted the attention of researchers in the last decade. Feature extraction
is one important step in expression analysis which contributes toward fast and
accurate expression recognition. This paper represents an approach of combining
the shape and appearance features to form a hybrid feature vector. We have
extracted Pyramid of Histogram of Gradients (PHOG) as shape descriptors and
Local Binary Patterns (LBP) as appearance features. The proposed framework
involves a novel approach of extracting hybrid features from active facial
patches. The active facial patches are located on the face regions which
undergo a major change during different expressions. After detection of facial
landmarks, the active patches are localized and hybrid features are calculated
from these patches. The use of small parts of face instead of the whole face
for extracting features reduces the computational cost and prevents the
over-fitting of the features for classification. By using linear discriminant
analysis, the dimensionality of the feature is reduced which is further
classified by using the support vector machine (SVM). The experimental results
on two publicly available databases show promising accuracy in recognizing all
expression classes.
Automatic Facial Expression Recognition Using Features of Salient Facial Patches
Authors: S. L. Happy, Aurobinda Routray
Date: 2015-05-15 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER), Facial Landmark Detection, General Classification

Extraction of discriminative features from salient facial patches plays a
vital role in effective facial expression recognition. The accurate detection
of facial landmarks improves the localization of the salient patches on face
images. This paper proposes a novel framework for expression recognition by
using appearance features of selected facial patches. A few prominent facial
patches, depending on the position of facial landmarks, are extracted which are
active during emotion elicitation. These active patches are further processed
to obtain the salient patches which contain discriminative features for
classification of each pair of expressions, thereby selecting different facial
patches as salient for different pair of expression classes. One-against-one
classification method is adopted using these features. In addition, an
automated learning-free facial landmark detection technique has been proposed,
which achieves similar performances as that of other state-of-art landmark
detection methods, yet requires significantly less execution time. The proposed
method is found to perform well consistently in different resolutions, hence,
providing a solution for expression recognition in low resolution images.
Experiments on CK+ and JAFFE facial expression databases show the effectiveness
of the proposed system.
An Image is Worth More than a Thousand Favorites: Surfacing the Hidden Beauty of Flickr Pictures
Authors: Rossano Schifanella, Miriam Redi, Luca Aiello
Date: 2015-05-13 00:00:00

The dynamics of attention in social media tend to obey power laws. Attention
concentrates on a relatively small number of popular items and neglecting the
vast majority of content produced by the crowd. Although popularity can be an
indication of the perceived value of an item within its community, previous
research has hinted to the fact that popularity is distinct from intrinsic
quality. As a result, content with low visibility but high quality lurks in the
tail of the popularity distribution. This phenomenon can be particularly
evident in the case of photo-sharing communities, where valuable photographers
who are not highly engaged in online social interactions contribute with
high-quality pictures that remain unseen. We propose to use a computer vision
method to surface beautiful pictures from the immense pool of
near-zero-popularity items, and we test it on a large dataset of
creative-commons photos on Flickr. By gathering a large crowdsourced ground
truth of aesthetics scores for Flickr images, we show that our method retrieves
photos whose median perceived beauty score is equal to the most popular ones,
and whose average is lower by only 1.5%.
Compound Poisson Processes, Latent Shrinkage Priors and Bayesian Nonconvex Penalization
Authors: Zhihua Zhang, Jin Li
Date: 2013-08-28 00:00:00
Tasks: regression, Sparse Learning

In this paper we discuss Bayesian nonconvex penalization for sparse learning
problems. We explore a nonparametric formulation for latent shrinkage
parameters using subordinators which are one-dimensional L\'{e}vy processes. We
particularly study a family of continuous compound Poisson subordinators and a
family of discrete compound Poisson subordinators. We exemplify four specific
subordinators: Gamma, Poisson, negative binomial and squared Bessel
subordinators. The Laplace exponents of the subordinators are Bernstein
functions, so they can be used as sparsity-inducing nonconvex penalty
functions. We exploit these subordinators in regression problems, yielding a
hierarchical model with multiple regularization parameters. We devise ECME
(Expectation/Conditional Maximization Either) algorithms to simultaneously
estimate regression coefficients and regularization parameters. The empirical
evaluation of simulated data shows that our approach is feasible and effective
in high-dimensional data analysis.
Ensembling classification models based on phalanxes of variables with applications in drug discovery
Authors: Jabed H. Tomal, William J. Welch, Ruben H. Zamar
Date: 2013-03-20 00:00:00
Tasks: Computational chemistry, Drug Discovery, General Classification

Statistical detection of a rare class of objects in a two-class
classification problem can pose several challenges. Because the class of
interest is rare in the training data, there is relatively little information
in the known class response labels for model building. At the same time the
available explanatory variables are often moderately high dimensional. In the
four assays of our drug-discovery application, compounds are active or not
against a specific biological target, such as lung cancer tumor cells, and
active compounds are rare. Several sets of chemical descriptor variables from
computational chemistry are available to classify the active versus inactive
class; each can have up to thousands of variables characterizing molecular
structure of the compounds. The statistical challenge is to make use of the
richness of the explanatory variables in the presence of scant response
information. Our algorithm divides the explanatory variables into subsets
adaptively and passes each subset to a base classifier. The various base
classifiers are then ensembled to produce one model to rank new objects by
their estimated probabilities of belonging to the rare class of interest. The
essence of the algorithm is to choose the subsets such that variables in the
same group work well together; we call such groups phalanxes.
Using Ensemble Models in the Histological Examination of Tissue Abnormalities
Authors: Giancarlo Crocetti, Michael Coakley, Phil Dressner, Wanda Kellum, Tamba Lamin
Date: 2015-05-15 00:00:00
Tasks: General Classification

Classification models for the automatic detection of abnormalities on
histological samples do exists, with an active debate on the cost associated
with false negative diagnosis (underdiagnosis) and false positive diagnosis
(overdiagnosis). Current models tend to underdiagnose, failing to recognize a
potentially fatal disease.
  The objective of this study is to investigate the possibility of
automatically identifying abnormalities in tissue samples through the use of an
ensemble model on data generated by histological examination and to minimize
the number of false negative cases.
A Multivariate Biomarker for Parkinson's Disease
Authors: Giancarlo Crocetti, Michael Coakley, Phil Dressner, Wanda Kellum, Tamba Lamin
Date: 2015-05-15 00:00:00
Tasks: General Classification

In this study, we executed a genomic analysis with the objective of selecting
a set of genes (possibly small) that would help in the detection and
classification of samples from patients affected by Parkinson Disease. We
performed a complete data analysis and during the exploratory phase, we
selected a list of differentially expressed genes. Despite their association
with the diseased state, we could not use them as a biomarker tool. Therefore,
our research was extended to include a multivariate analysis approach resulting
in the identification and selection of a group of 20 genes that showed a clear
potential in detecting and correctly classify Parkinson Disease samples even in
the presence of other neurodegenerative disorders.
General Riemannian SOM
Authors: Jascha A. Schewtschenko
Date: 2015-05-14 00:00:00

Kohonen's Self-Organizing Maps (SOMs) have proven to be a successful
data-reduction method to identify the intrinsic lower-dimensional sub-manifold
of a data set that is scattered in the higher-dimensional feature space.
Motivated by the possibly non-Euclidian nature of the feature space and of the
intrinsic geometry of the data set, we extend the definition of classic SOMs to
obtain the General Riemannian SOM (GRiSOM). We additionally provide an
implementation as a proof-of-concept for geometries with constant curvature. We
furthermore perform the analytic and numerical analysis of the stability limits
of certain (GRi)SOM configurations covering the different possible regular
tessellation of the map space in each geometry. A deviation between the
numerical and analytic stability limit has been observed for the square and
hexagonal Euclidean maps for very small neighbourhoods in the map space as well
as agreement in case of longer-ranged relations between the map nodes.
Training generative neural networks via Maximum Mean Discrepancy optimization
Authors: Gintare Karolina Dziugaite, Daniel M. Roy, Zoubin Ghahramani
Date: 2015-05-14 00:00:00

We consider training a deep neural network to generate samples from an
unknown distribution given i.i.d. data. We frame learning as an optimization
minimizing a two-sample test statistic---informally speaking, a good generator
network produces samples that cause a two-sample test to fail to reject the
null hypothesis. As our two-sample test statistic, we use an unbiased estimate
of the maximum mean discrepancy, which is the centerpiece of the nonparametric
kernel two-sample test proposed by Gretton et al. (2012). We compare to the
adversarial nets framework introduced by Goodfellow et al. (2014), in which
learning is a two-player game between a generator network and an adversarial
discriminator network, both trained to outwit the other. From this perspective,
the MMD statistic plays the role of the discriminator. In addition to empirical
comparisons, we prove bounds on the generalization error incurred by optimizing
the empirical MMD.
Petuum: A New Platform for Distributed Machine Learning on Big Data
Authors: Eric P. Xing, Qirong Ho, Wei Dai, Jin Kyu Kim, Jinliang Wei, Seunghak Lee, Xun Zheng, Pengtao Xie, Abhimanu Kumar, Yao-Liang Yu
Date: 2013-12-30 00:00:00
Tasks: BIG-bench Machine Learning, Scheduling

What is a systematic way to efficiently apply a wide spectrum of advanced ML
programs to industrial scale problems, using Big Models (up to 100s of billions
of parameters) on Big Data (up to terabytes or petabytes)? Modern
parallelization strategies employ fine-grained operations and scheduling beyond
the classic bulk-synchronous processing paradigm popularized by MapReduce, or
even specialized graph-based execution that relies on graph representations of
ML programs. The variety of approaches tends to pull systems and algorithms
design in different directions, and it remains difficult to find a universal
platform applicable to a wide range of ML programs at scale. We propose a
general-purpose framework that systematically addresses data- and
model-parallel challenges in large-scale ML, by observing that many ML programs
are fundamentally optimization-centric and admit error-tolerant,
iterative-convergent algorithmic solutions. This presents unique opportunities
for an integrative system design, such as bounded-error network synchronization
and dynamic scheduling based on ML program structure. We demonstrate the
efficacy of these system designs versus well-known implementations of modern ML
algorithms, allowing ML programs to run in much less time and at considerably
larger model sizes, even on modestly-sized compute clusters.
Task-Based Optimization of Computed Tomography Imaging Systems
Authors: Adrian A. Sanchez
Date: 2015-05-14 00:00:00
Tasks: Image Reconstruction

The goal of this thesis is to provide a framework for the use of task-based
metrics of image quality to aid in the design, implementation, and evaluation
of CT image reconstruction algorithms and CT systems in general. We support the
view that task-based metrics of image quality can be useful in guiding the
algorithm design and implementation process in order to yield images of
objectively superior quality and higher utility for a given task. Further, we
believe that metrics such as the Hotelling observer (HO) SNR can be used as
summary scalar metrics of image quality for the evaluation of images produced
by novel reconstruction algorithms. In this work, we aim to construct a concise
and versatile formalism for image reconstruction algorithm design,
implementation, and assessment. The bulk of the work focuses on linear
analytical algorithms, specifically the ubiquitous filtered back-projection
(FBP) algorithm. However, due to the demonstrated importance of
optimization-based algorithms in a wide variety of CT applications, we devote
one chapter to the characterization of noise properties in TV-based iterative
reconstruction, as the understanding of image statistics in optimization-based
reconstruction is the limiting factor in applying HO metrics.
Improving Image Classification with Location Context
Authors: Kevin Tang, Manohar Paluri, Li Fei-Fei, Rob Fergus, Lubomir Bourdev
Date: 2015-05-14 00:00:00
Proceeding: ICCV 2015 12
Tasks: Classification, General Classification, image-classification, Image Classification

With the widespread availability of cellphones and cameras that have GPS
capabilities, it is common for images being uploaded to the Internet today to
have GPS coordinates associated with them. In addition to research that tries
to predict GPS coordinates from visual features, this also opens up the door to
problems that are conditioned on the availability of GPS coordinates. In this
work, we tackle the problem of performing image classification with location
context, in which we are given the GPS coordinates for images in both the train
and test phases. We explore different ways of encoding and extracting features
from the GPS coordinates, and show how to naturally incorporate these features
into a Convolutional Neural Network (CNN), the current state-of-the-art for
most image classification and recognition problems. We also show how it is
possible to simultaneously learn the optimal pooling radii for a subset of our
features within the CNN framework. To evaluate our model and to help promote
research in this area, we identify a set of location-sensitive concepts and
annotate a subset of the Yahoo Flickr Creative Commons 100M dataset that has
GPS coordinates with these concepts, which we make publicly available. By
leveraging location context, we are able to achieve almost a 7% gain in mean
average precision.
Recursive Neural Networks Can Learn Logical Semantics
Authors: Samuel R. Bowman, Christopher Potts, Christopher D. Manning
Date: 2014-06-06 00:00:00
Proceeding: WS 2015 7
Tasks: Open-Ended Question Answering, Relational Reasoning, Sentence, Tensor Networks

Tree-structured recursive neural networks (TreeRNNs) for sentence meaning
have been successful for many applications, but it remains an open question
whether the fixed-length representations that they learn can support tasks as
demanding as logical deduction. We pursue this question by evaluating whether
two such models---plain TreeRNNs and tree-structured neural tensor networks
(TreeRNTNs)---can correctly learn to identify logical relationships such as
entailment and contradiction using these representations. In our first set of
experiments, we generate artificial data from a logical grammar and use it to
evaluate the models' ability to learn to handle basic relational reasoning,
recursive structures, and quantification. We then evaluate the models on the
more natural SICK challenge data. Both models perform competitively on the SICK
data and generalize well in all three experiments on simulated data, suggesting
that they can learn suitable representations for logical inference in natural
language.
Non-unique games over compact groups and orientation estimation in cryo-EM
Authors: Afonso S. Bandeira, Yutong Chen, Amit Singer
Date: 2015-05-14 00:00:00

Let $\mathcal{G}$ be a compact group and let $f_{ij} \in L^2(\mathcal{G})$.
We define the Non-Unique Games (NUG) problem as finding $g_1,\dots,g_n \in
\mathcal{G}$ to minimize $\sum_{i,j=1}^n f_{ij} \left( g_i g_j^{-1}\right)$. We
devise a relaxation of the NUG problem to a semidefinite program (SDP) by
taking the Fourier transform of $f_{ij}$ over $\mathcal{G}$, which can then be
solved efficiently. The NUG framework can be seen as a generalization of the
little Grothendieck problem over the orthogonal group and the Unique Games
problem and includes many practically relevant problems, such as the maximum
likelihood estimator} to registering bandlimited functions over the unit sphere
in $d$-dimensions and orientation estimation in cryo-Electron Microscopy.
Parametric Regression on the Grassmannian
Authors: Yi Hong, Nikhil Singh, Roland Kwitt, Nuno Vasconcelos, Marc Niethammer
Date: 2015-05-14 00:00:00
Tasks: Crowd Counting, regression

We address the problem of fitting parametric curves on the Grassmann manifold
for the purpose of intrinsic parametric regression. As customary in the
literature, we start from the energy minimization formulation of linear
least-squares in Euclidean spaces and generalize this concept to general
nonflat Riemannian manifolds, following an optimal-control point of view. We
then specialize this idea to the Grassmann manifold and demonstrate that it
yields a simple, extensible and easy-to-implement solution to the parametric
regression problem. In fact, it allows us to extend the basic geodesic model to
(1) a time-warped variant and (2) cubic splines. We demonstrate the utility of
the proposed solution on different vision problems, such as shape regression as
a function of age, traffic-speed estimation and crowd-counting from
surveillance video clips. Most notably, these problems can be conveniently
solved within the same framework without any specifically-tailored steps along
the processing pipeline.
Unsupervised Object Discovery and Tracking in Video Collections
Authors: Suha Kwak, Minsu Cho, Ivan Laptev, Jean Ponce, Cordelia Schmid
Date: 2015-05-14 00:00:00
Proceeding: ICCV 2015 12
Tasks: Object, Object Discovery, Video Understanding

This paper addresses the problem of automatically localizing dominant objects
as spatio-temporal tubes in a noisy collection of videos with minimal or even
no supervision. We formulate the problem as a combination of two complementary
processes: discovery and tracking. The first one establishes correspondences
between prominent regions across videos, and the second one associates
successive similar object regions within the same video. Interestingly, our
algorithm also discovers the implicit topology of frames associated with
instances of the same object class across different videos, a role normally
left to supervisory information in the form of class labels in conventional
image and video understanding methods. Indeed, as demonstrated by our
experiments, our method can handle video collections featuring multiple object
classes, and substantially outperforms the state of the art in colocalization,
even though it tackles a broader problem with much less supervision.