pwc-archive/papers-with-abstracts huggingface dataset:
Robust Statistical Approach for Extraction of Moving Human Silhouettes from Videos
Authors: Oinam Binarani Devi, Nissi S. Paul, Y. Jayanta Singh
Date: 2014-08-17 00:00:00
Tasks: Action Classification, Action Recognition, Pose Estimation, Temporal Action Localization

Human pose estimation is one of the key problems in computer vision that has
been studied in the recent years. The significance of human pose estimation is
in the higher level tasks of understanding human actions applications such as
recognition of anomalous actions present in videos and many other related
applications. The human poses can be estimated by extracting silhouettes of
humans as silhouettes are robust to variations and it gives the shape
information of the human body. Some common challenges include illumination
changes, variation in environments, and variation in human appearances. Thus
there is a need for a robust method for human pose estimation. This paper
presents a study and analysis of approaches existing for silhouette extraction
and proposes a robust technique for extracting human silhouettes in video
sequences. Gaussian Mixture Model (GMM) A statistical approach is combined with
HSV (Hue, Saturation and Value) color space model for a robust background model
that is used for background subtraction to produce foreground blobs, called
human silhouettes. Morphological operations are then performed on foreground
blobs from background subtraction. The silhouettes obtained from this work can
be used in further tasks associated with human action interpretation and
activity processes like human action classification, human pose estimation and
action recognition or action interpretation.
On solving Ordinary Differential Equations using Gaussian Processes
Authors: David Barber
Date: 2014-08-17 00:00:00
Tasks: Gaussian Processes

We describe a set of Gaussian Process based approaches that can be used to
solve non-linear Ordinary Differential Equations. We suggest an explicit
probabilistic solver and two implicit methods, one analogous to Picard
iteration and the other to gradient matching. All methods have greater accuracy
than previously suggested Gaussian Process approaches. We also suggest a
general approach that can yield error estimates from any standard ODE solver.
Real-time emotion recognition for gaming using deep convolutional network features
Authors: Sébastien Ouellet
Date: 2014-08-16 00:00:00
Tasks: Emotion Recognition

The goal of the present study is to explore the application of deep
convolutional network features to emotion recognition. Results indicate that
they perform similarly to other published models at a best recognition rate of
94.4%, and do so with a single still image rather than a video stream. An
implementation of an affective feedback game is also described, where a
classifier using these features tracks the facial expressions of a player in
real-time.
A fast patch-dictionary method for whole image recovery
Authors: Yangyang Xu, Wotao Yin
Date: 2014-08-16 00:00:00
Tasks: Compressive Sensing, Deblurring, Denoising, Dictionary Learning, Image Deblurring, Image Inpainting, Super-Resolution

Various algorithms have been proposed for dictionary learning. Among those
for image processing, many use image patches to form dictionaries. This paper
focuses on whole-image recovery from corrupted linear measurements. We address
the open issue of representing an image by overlapping patches: the overlapping
leads to an excessive number of dictionary coefficients to determine. With very
few exceptions, this issue has limited the applications of image-patch methods
to the local kind of tasks such as denoising, inpainting, cartoon-texture
decomposition, super-resolution, and image deblurring, for which one can
process a few patches at a time. Our focus is global imaging tasks such as
compressive sensing and medical image recovery, where the whole image is
encoded together, making it either impossible or very ineffective to update a
few patches at a time.
  Our strategy is to divide the sparse recovery into multiple subproblems, each
of which handles a subset of non-overlapping patches, and then the results of
the subproblems are averaged to yield the final recovery. This simple strategy
is surprisingly effective in terms of both quality and speed. In addition, we
accelerate computation of the learned dictionary by applying a recent block
proximal-gradient method, which not only has a lower per-iteration complexity
but also takes fewer iterations to converge, compared to the current
state-of-the-art. We also establish that our algorithm globally converges to a
stationary point. Numerical results on synthetic data demonstrate that our
algorithm can recover a more faithful dictionary than two state-of-the-art
methods.
  Combining our whole-image recovery and dictionary-learning methods, we
numerically simulate image inpainting, compressive sensing recovery, and
deblurring. Our recovery is more faithful than those of a total variation
method and a method based on overlapping patches.
Analysis of a chaotic spiking neural model: The NDS neuron
Authors: Mohammad Alhawarat, Waleed Nazih, Mohammad Eldesouki
Date: 2014-08-16 00:00:00

Further analysis and experimentation is carried out in this paper for a
chaotic dynamic model, viz. the Nonlinear Dynamic State neuron (NDS). The
analysis and experimentations are performed to further understand the
underlying dynamics of the model and enhance it as well. Chaos provides many
interesting properties that can be exploited to achieve computational tasks.
Such properties are sensitivity to initial conditions, space filling, control
and synchronization.Chaos might play an important role in information
processing tasks in human brain as suggested by biologists. If artificial
neural networks (ANNs) is equipped with chaos then it will enrich the dynamic
behaviours of such networks. The NDS model has some limitations and can be
overcome in different ways. In this paper different approaches are followed to
push the boundaries of the NDS model in order to enhance it. One way is to
study the effects of scaling the parameters of the chaotic equations of the NDS
model and study the resulted dynamics. Another way is to study the method that
is used in discretization of the original R\"{o}ssler that the NDS model is
based on. These approaches have revealed some facts about the NDS attractor and
suggest why such a model can be stabilized to large number of unstable periodic
orbits (UPOs) which might correspond to memories in phase space.
Multi-Sensor Event Detection using Shape Histograms
Authors: Ehtesham Hassan, Gautam Shroff, Puneet Agarwal
Date: 2014-08-16 00:00:00
Tasks: Event Detection, Time Series, Time Series Analysis

Vehicular sensor data consists of multiple time-series arising from a number
of sensors. Using such multi-sensor data we would like to detect occurrences of
specific events that vehicles encounter, e.g., corresponding to particular
maneuvers that a vehicle makes or conditions that it encounters. Events are
characterized by similar waveform patterns re-appearing within one or more
sensors. Further such patterns can be of variable duration. In this work, we
propose a method for detecting such events in time-series data using a novel
feature descriptor motivated by similar ideas in image processing. We define
the shape histogram: a constant dimension descriptor that nevertheless captures
patterns of variable duration. We demonstrate the efficacy of using shape
histograms as features to detect events in an SVM-based, multi-sensor,
supervised learning scenario, i.e., multiple time-series are used to detect an
event. We present results on real-life vehicular sensor data and show that our
technique performs better than available pattern detection implementations on
our data, and that it can also be used to combine features from multiple
sensors resulting in better accuracy than using any single sensor. Since
previous work on pattern detection in time-series has been in the single series
context, we also present results using our technique on multiple standard
time-series datasets and show that it is the most versatile in terms of how it
ranks compared to other published results.
Robust 3D face recognition in presence of pose and partial occlusions or missing parts
Authors: Parama Bagchi, Debotosh Bhattacharjee, Mita Nasipuri
Date: 2014-08-16 00:00:00
Tasks: Face Recognition

In this paper, we propose a robust 3D face recognition system which can handle pose as well as occlusions in real world. The system at first takes as input, a 3D range image, simultaneously registers it using ICP(Iterative Closest Point) algorithm. ICP used in this work, registers facial surfaces to a common model by minimizing distances between a probe model and a gallery model. However the performance of ICP relies heavily on the initial conditions. Hence, it is necessary to provide an initial registration, which will be improved iteratively and finally converge to the best alignment possible. Once the faces are registered, the occlusions are automatically extracted by thresholding the depth map values of the 3D image. After the occluded regions are detected, restoration is done by Principal Component Analysis (PCA). The restored images, after the removal of occlusions, are then fed to the recognition system for classification purpose. Features are extracted from the reconstructed non-occluded face images in the form of face normals. The experimental results which were obtained on the occluded facial images from the Bosphorus 3D face database, illustrate that our occlusion compensation scheme has attained a recognition accuracy of 91.30%.
Turkish Presidential Elections TRT Publicity Speech Facial Expression Analysis
Authors: H. Emrah Tasli, Paul Ivan
Date: 2014-08-15 00:00:00

In this paper, facial expressions of the three Turkish presidential
candidates Demirtas, Erdogan and Ihsanoglu (in alphabetical order) are analyzed
during the publicity speeches featured at TRT (Turkish Radio and Television) on
03.08.2014. FaceReader is used for the analysis where 3D modeling of the face
is achieved using the active appearance models (AAM). Over 500 landmark points
are tracked and analyzed for obtaining the facial expressions during the whole
speech. All source videos and the data are publicly available for research
purposes.
Detection is the central problem in real-word spelling correction
Authors: L. Amber Wilcox-O'Hearn
Date: 2014-08-13 00:00:00
Tasks: Sentence, Spelling Correction

Real-word spelling correction differs from non-word spelling correction in
its aims and its challenges. Here we show that the central problem in real-word
spelling correction is detection. Methods from non-word spelling correction,
which focus instead on selection among candidate corrections, do not address
detection adequately, because detection is either assumed in advance or heavily
constrained. As we demonstrate in this paper, merely discriminating between the
intended word and a random close variation of it within the context of a
sentence is a task that can be performed with high accuracy using
straightforward models. Trigram models are sufficient in almost all cases. The
difficulty comes when every word in the sentence is a potential error, with a
large set of possible candidate corrections. Despite their strengths, trigram
models cannot reliably find true errors without introducing many more, at least
not when used in the obvious sequential way without added structure. The
detection task exposes weakness not visible in the selection task.
Indexing Cost Sensitive Prediction
Authors: Leilani Battle, Edward Benson, Aditya Parameswaran, Eugene Wu
Date: 2014-08-15 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making, Prediction

Predictive models are often used for real-time decision making. However,
typical machine learning techniques ignore feature evaluation cost, and focus
solely on the accuracy of the machine learning models obtained utilizing all
the features available. We develop algorithms and indexes to support
cost-sensitive prediction, i.e., making decisions using machine learning models
taking feature evaluation cost into account. Given an item and a online
computation cost (i.e., time) budget, we present two approaches to return an
appropriately chosen machine learning model that will run within the specified
time on the given item. The first approach returns the optimal machine learning
model, i.e., one with the highest accuracy, that runs within the specified
time, but requires significant up-front precomputation time. The second
approach returns a possibly sub- optimal machine learning model, but requires
little up-front precomputation time. We study these two algorithms in detail
and characterize the scenarios (using real and synthetic data) in which each
performs well. Unlike prior work that focuses on a narrow domain or a specific
algorithm, our techniques are very general: they apply to any cost-sensitive
prediction scenario on any machine learning algorithm.
Evaluating Visual Properties via Robust HodgeRank
Authors: Qianqian Xu, Jiechao Xiong, Xiaochun Cao, Qingming Huang, Yuan YAO
Date: 2014-08-15 00:00:00
Tasks: Graph Sampling, Outlier Detection

Nowadays, how to effectively evaluate visual properties has become a popular topic for fine-grained visual comprehension. In this paper we study the problem of how to estimate such visual properties from a ranking perspective with the help of the annotators from online crowdsourcing platforms. The main challenges of our task are two-fold. On one hand, the annotations often contain contaminated information, where a small fraction of label flips might ruin the global ranking of the whole dataset. On the other hand, considering the large data capacity, the annotations are often far from being complete. What is worse, there might even exist imbalanced annotations where a small subset of samples are frequently annotated. Facing such challenges, we propose a robust ranking framework based on the principle of Hodge decomposition of imbalanced and incomplete ranking data. According to the HodgeRank theory, we find that the major source of the contamination comes from the cyclic ranking component of the Hodge decomposition. This leads us to an outlier detection formulation as sparse approximations of the cyclic ranking projection. Taking a step further, it facilitates a novel outlier detection model as Huber's LASSO in robust statistics. Moreover, simple yet scalable algorithms are developed based on Linearized Bregman Iteration to achieve an even less biased estimator. Statistical consistency of outlier detection is established in both cases under nearly the same conditions. Our studies are supported by experiments with both simulated examples and real-world data. The proposed framework provides us a promising tool for robust ranking with large scale crowdsourcing data arising from computer vision.
Classifiers fusion method to recognize handwritten persian numerals
Authors: Reza Azad, Babak Azad, Iraj Mogharreb, Shahram Jamali
Date: 2014-07-09 00:00:00

Recognition of Persian handwritten characters has been considered as a
significant field of research for the last few years under pattern analysing
technique. In this paper, a new approach for robust handwritten Persian
numerals recognition using strong feature set and a classifier fusion method is
scrutinized to increase the recognition percentage. For implementing the
classifier fusion technique, we have considered k nearest neighbour (KNN),
linear classifier (LC) and support vector machine (SVM) classifiers. The
innovation of this tactic is to attain better precision with few features using
classifier fusion method. For evaluation of the proposed method we considered a
Persian numerals database with 20,000 handwritten samples. Spending 15,000
samples for training stage, we verified our technique on other 5,000 samples,
and the correct recognition ratio achieved approximately 99.90%. Additional, we
got 99.97% exactness using four-fold cross validation procedure on 20,000
databases.
SimLex-999: Evaluating Semantic Models with (Genuine) Similarity Estimation
Authors: Felix Hill, Roi Reichart, Anna Korhonen
Date: 2014-08-15 00:00:00
Proceeding: CL 2015 12
Tasks: Representation Learning

We present SimLex-999, a gold standard resource for evaluating distributional
semantic models that improves on existing resources in several important ways.
First, in contrast to gold standards such as WordSim-353 and MEN, it explicitly
quantifies similarity rather than association or relatedness, so that pairs of
entities that are associated but not actually similar [Freud, psychology] have
a low rating. We show that, via this focus on similarity, SimLex-999
incentivizes the development of models with a different, and arguably wider
range of applications than those which reflect conceptual association. Second,
SimLex-999 contains a range of concrete and abstract adjective, noun and verb
pairs, together with an independent rating of concreteness and (free)
association strength for each pair. This diversity enables fine-grained
analyses of the performance of models on concepts of different types, and
consequently greater insight into how architectures can be improved. Further,
unlike existing gold standard evaluations, for which automatic approaches have
reached or surpassed the inter-annotator agreement ceiling, state-of-the-art
models perform well below this ceiling on SimLex-999. There is therefore plenty
of scope for SimLex-999 to quantify future improvements to distributional
semantic models, guiding the development of the next generation of
representation-learning architectures.
Statistical Constraints
Authors: Roberto Rossi, Steven Prestwich, S. Armagan Tarim
Date: 2014-02-20 00:00:00
Tasks: Scheduling

We introduce statistical constraints, a declarative modelling tool that links
statistics and constraint programming. We discuss two statistical constraints
and some associated filtering algorithms. Finally, we illustrate applications
to standard problems encountered in statistics and to a novel inspection
scheduling problem in which the aim is to find inspection plans with desirable
statistical properties.
A convex pseudo-likelihood framework for high dimensional partial correlation estimation with convergence guarantees
Authors: Kshitij Khare, Sang-Yun Oh, Bala Rajaratnam
Date: 2013-07-20 00:00:00
Tasks: Model Selection, regression

Sparse high dimensional graphical model selection is a topic of much interest
in modern day statistics. A popular approach is to apply l1-penalties to either
(1) parametric likelihoods, or, (2) regularized regression/pseudo-likelihoods,
with the latter having the distinct advantage that they do not explicitly
assume Gaussianity. As none of the popular methods proposed for solving
pseudo-likelihood based objective functions have provable convergence
guarantees, it is not clear if corresponding estimators exist or are even
computable, or if they actually yield correct partial correlation graphs. This
paper proposes a new pseudo-likelihood based graphical model selection method
that aims to overcome some of the shortcomings of current methods, but at the
same time retain all their respective strengths. In particular, we introduce a
novel framework that leads to a convex formulation of the partial covariance
regression graph problem, resulting in an objective function comprised of
quadratic forms. The objective is then optimized via a coordinate-wise
approach. The specific functional form of the objective function facilitates
rigorous convergence analysis leading to convergence guarantees; an important
property that cannot be established using standard results, when the dimension
is larger than the sample size, as is often the case in high dimensional
applications. These convergence guarantees ensure that estimators are
well-defined under very general conditions, and are always computable. In
addition, the approach yields estimators that have good large sample properties
and also respect symmetry. Furthermore, application to simulated/real data,
timing comparisons and numerical convergence is demonstrated. We also present a
novel unifying framework that places all graphical pseudo-likelihood methods as
special cases of a more general formulation, leading to important insights.
An Adaptive Statistical Non-uniform Quantizer for Detail Wavelet Components in Lossy JPEG2000 Image Compression
Authors: Madhur Srivastava, Satish K. Singh, Prasanta K. Panigrahi
Date: 2013-05-09 00:00:00
Tasks: Image Compression, Quantization

The paper presents a non-uniform quantization method for the Detail
components in the JPEG2000 standard. Incorporating the fact that the
coefficients lying towards the ends of the histogram plot of each Detail
component represent the structural information of an image, the quantization
step sizes become smaller at they approach the ends of the histogram plot. The
variable quantization step sizes are determined by the actual statistics of the
wavelet coefficients. Mean and standard deviation are the two statistical
parameters used iteratively to obtain the variable step sizes. Moreover, the
mean of the coefficients lying within the step size is chosen as the quantized
value, contrary to the deadzone uniform quantizer which selects the midpoint of
the quantization step size as the quantized value. The experimental results of
the deadzone uniform quantizer and the proposed non-uniform quantizer are
objectively compared by using Mean-Squared Error (MSE) and Mean Structural
Similarity Index Measure (MSSIM), to evaluate the quantization error and
reconstructed image quality, respectively. Subjective analysis of the
reconstructed images is also carried out. Through the objective and subjective
assessments, it is shown that the non-uniform quantizer performs better than
the deadzone uniform quantizer in the perceptual quality of the reconstructed
image, especially at low bitrates. More importantly, unlike the deadzone
uniform quantizer, the non-uniform quantizer accomplishes better visual quality
with a few quantized values.
Likely to stop? Predicting Stopout in Massive Open Online Courses
Authors: Colin Taylor, Kalyan Veeramachaneni, Una-May O'Reilly
Date: 2014-08-14 00:00:00
Tasks: Feature Importance, Prediction

Understanding why students stopout will help in understanding how students
learn in MOOCs. In this report, part of a 3 unit compendium, we describe how we
build accurate predictive models of MOOC student stopout. We document a
scalable, stopout prediction methodology, end to end, from raw source data to
model analysis. We attempted to predict stopout for the Fall 2012 offering of
6.002x. This involved the meticulous and crowd-sourced engineering of over 25
predictive features extracted for thousands of students, the creation of
temporal and non-temporal data representations for use in predictive modeling,
the derivation of over 10 thousand models with a variety of state-of-the-art
machine learning techniques and the analysis of feature importance by examining
over 70000 models. We found that stop out prediction is a tractable problem.
Our models achieved an AUC (receiver operating characteristic
area-under-the-curve) as high as 0.95 (and generally 0.88) when predicting one
week in advance. Even with more difficult prediction problems, such as
predicting stop out at the end of the course with only one weeks' data, the
models attained AUCs of 0.7.