pwc-archive/papers-with-abstracts huggingface dataset:
HEp-2 Cell Classification: The Role of Gaussian Scale Space Theory as A Pre-processing Approach
Authors: Xianbiao Qi, Guoying Zhao, Jie Chen, Matti Pietikäinen
Date: 2015-09-08 00:00:00
Tasks: General Classification

\textit{Indirect Immunofluorescence Imaging of Human Epithelial Type 2}
(HEp-2) cells is an effective way to identify the presence of Anti-Nuclear
Antibody (ANA). Most existing works on HEp-2 cell classification mainly focus
on feature extraction, feature encoding and classifier design. Very few efforts
have been devoted to study the importance of the pre-processing techniques. In
this paper, we analyze the importance of the pre-processing, and investigate
the role of Gaussian Scale Space (GSS) theory as a pre-processing approach for
the HEp-2 cell classification task. We validate the GSS pre-processing under
the Local Binary Pattern (LBP) and the Bag-of-Words (BoW) frameworks. Under the
BoW framework, the introduced pre-processing approach, using only one Local
Orientation Adaptive Descriptor (LOAD), achieved superior performance on the
Executable Thematic on Pattern Recognition Techniques for Indirect
Immunofluorescence (ET-PRT-IIF) image analysis. Our system, using only one
feature, outperformed the winner of the ICPR 2014 contest that combined four
types of features. Meanwhile, the proposed pre-processing method is not
restricted to this work; it can be generalized to many existing works.
Object Proposals for Text Extraction in the Wild
Authors: Lluis Gomez, Dimosthenis Karatzas
Date: 2015-09-08 00:00:00
Tasks: Object

Object Proposals is a recent computer vision technique receiving increasing
interest from the research community. Its main objective is to generate a
relatively small set of bounding box proposals that are most likely to contain
objects of interest. The use of Object Proposals techniques in the scene text
understanding field is innovative. Motivated by the success of powerful while
expensive techniques to recognize words in a holistic way, Object Proposals
techniques emerge as an alternative to the traditional text detectors.
  In this paper we study to what extent the existing generic Object Proposals
methods may be useful for scene text understanding. Also, we propose a new
Object Proposals algorithm that is specifically designed for text and compare
it with other generic methods in the state of the art. Experiments show that
our proposal is superior in its ability of producing good quality word
proposals in an efficient way. The source code of our method is made publicly
available.
Deeply Learning the Messages in Message Passing Inference
Authors: Guosheng Lin, Chunhua Shen, Ian Reid, Anton Van Den Hengel
Date: 2015-06-06 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Image Segmentation, Semantic Segmentation, Structured Prediction

Deep structured output learning shows great promise in tasks like semantic
image segmentation. We proffer a new, efficient deep structured model learning
scheme, in which we show how deep Convolutional Neural Networks (CNNs) can be
used to estimate the messages in message passing inference for structured
prediction with Conditional Random Fields (CRFs). With such CNN message
estimators, we obviate the need to learn or evaluate potential functions for
message calculation. This confers significant efficiency for learning, since
otherwise when performing structured learning for a CRF with CNN potentials it
is necessary to undertake expensive inference for every stochastic gradient
iteration. The network output dimension for message estimation is the same as
the number of classes, in contrast to the network output for general CNN
potential functions in CRFs, which is exponential in the order of the
potentials. Hence CNN message learning has fewer network parameters and is more
scalable for cases that a large number of classes are involved. We apply our
method to semantic image segmentation on the PASCAL VOC 2012 dataset. We
achieve an intersection-over-union score of 73.4 on its test set, which is the
best reported result for methods using the VOC training images alone. This
impressive performance demonstrates the effectiveness and usefulness of our CNN
message learning method.
Sampled Weighted Min-Hashing for Large-Scale Topic Mining
Authors: Gibran Fuentes-Pineda, Ivan Vladimir Meza-Ruiz
Date: 2015-09-06 00:00:00
Tasks: General Classification

We present Sampled Weighted Min-Hashing (SWMH), a randomized approach to
automatically mine topics from large-scale corpora. SWMH generates multiple
random partitions of the corpus vocabulary based on term co-occurrence and
agglomerates highly overlapping inter-partition cells to produce the mined
topics. While other approaches define a topic as a probabilistic distribution
over a vocabulary, SWMH topics are ordered subsets of such vocabulary.
Interestingly, the topics mined by SWMH underlie themes from the corpus at
different levels of granularity. We extensively evaluate the meaningfulness of
the mined topics both qualitatively and quantitatively on the NIPS (1.7 K
documents), 20 Newsgroups (20 K), Reuters (800 K) and Wikipedia (4 M) corpora.
Additionally, we compare the quality of SWMH with Online LDA topics for
document representation in classification.
Supervised Collective Classification for Crowdsourcing
Authors: Pin-Yu Chen, Chia-Wei Lien, Fu-Jen Chu, Pai-Shun Ting, Shin-Ming Cheng
Date: 2015-07-23 00:00:00
Tasks: Classification, General Classification

Crowdsourcing utilizes the wisdom of crowds for collective classification via
information (e.g., labels of an item) provided by labelers. Current
crowdsourcing algorithms are mainly unsupervised methods that are unaware of
the quality of crowdsourced data. In this paper, we propose a supervised
collective classification algorithm that aims to identify reliable labelers
from the training data (e.g., items with known labels). The reliability (i.e.,
weighting factor) of each labeler is determined via a saddle point algorithm.
The results on several crowdsourced data show that supervised methods can
achieve better classification accuracy than unsupervised methods, and our
proposed method outperforms other algorithms.
Dynamic Structure Embedded Online Multiple-Output Regression for Stream Data
Authors: Changsheng Li, Fan Wei, Weishan Dong, Qingshan Liu, Xiangfeng Wang, Xin Zhang
Date: 2014-12-18 00:00:00
Tasks: regression

Online multiple-output regression is an important machine learning technique
for modeling, predicting, and compressing multi-dimensional correlated data
streams. In this paper, we propose a novel online multiple-output regression
method, called MORES, for stream data. MORES can \emph{dynamically} learn the
structure of the coefficients change in each update step to facilitate the
model's continuous refinement. We observe that limited expressive ability of
the regression model, especially in the preliminary stage of online update,
often leads to the variables in the residual errors being dependent. In light
of this point, MORES intends to \emph{dynamically} learn and leverage the
structure of the residual errors to improve the prediction accuracy. Moreover,
we define three statistical variables to \emph{exactly} represent all the seen
samples for \emph{incrementally} calculating prediction loss in each online
update round, which can avoid loading all the training data into memory for
updating model, and also effectively prevent drastic fluctuation of the model
in the presence of noise. Furthermore, we introduce a forgetting factor to set
different weights on samples so as to track the data streams' evolving
characteristics quickly from the latest samples. Experiments on one synthetic
dataset and three real-world datasets validate the effectiveness of the
proposed method. In addition, the update speed of MORES is at least 2000
samples processed per second on the three real-world datasets, more than 15
times faster than the state-of-the-art online learning algorithm.
Enhancing Automatically Discovered Multi-level Acoustic Patterns Considering Context Consistency With Applications in Spoken Term Detection
Authors: Cheng-Tao Chung, Wei-Ning Hsu, Cheng-Yi Lee, Lin-shan Lee
Date: 2015-09-07 00:00:00

This paper presents a novel approach for enhancing the multiple sets of
acoustic patterns automatically discovered from a given corpus. In a previous
work it was proposed that different HMM configurations (number of states per
model, number of distinct models) for the acoustic patterns form a
two-dimensional space. Multiple sets of acoustic patterns automatically
discovered with the HMM configurations properly located on different points
over this two-dimensional space were shown to be complementary to one another,
jointly capturing the characteristics of the given corpus. By representing the
given corpus as sequences of acoustic patterns on different HMM sets, the
pattern indices in these sequences can be relabeled considering the context
consistency across the different sequences. Good improvements were observed in
preliminary experiments of pattern spoken term detection (STD) performed on
both TIMIT and Mandarin Broadcast News with such enhanced patterns.
Fuzzy Jets
Authors: Lester Mackey, Benjamin Nachman, Ariel Schwartzman, Conrad Stansbury
Date: 2015-09-07 00:00:00
Tasks: Clustering, Jet Tagging

Collimated streams of particles produced in high energy physics experiments
are organized using clustering algorithms to form jets. To construct jets, the
experimental collaborations based at the Large Hadron Collider (LHC) primarily
use agglomerative hierarchical clustering schemes known as sequential
recombination. We propose a new class of algorithms for clustering jets that
use infrared and collinear safe mixture models. These new algorithms, known as
fuzzy jets, are clustered using maximum likelihood techniques and can
dynamically determine various properties of jets like their size. We show that
the fuzzy jet size adds additional information to conventional jet tagging
variables. Furthermore, we study the impact of pileup and show that with some
slight modifications to the algorithm, fuzzy jets can be stable up to high
pileup interaction multiplicities.
Unsupervised Spoken Term Detection with Spoken Queries by Multi-level Acoustic Patterns with Varying Model Granularity
Authors: Cheng-Tao Chung, Chun-an Chan, Lin-shan Lee
Date: 2015-09-07 00:00:00

This paper presents a new approach for unsupervised Spoken Term Detection
with spoken queries using multiple sets of acoustic patterns automatically
discovered from the target corpus. The different pattern HMM
configurations(number of states per model, number of distinct models, number of
Gaussians per state)form a three-dimensional model granularity space. Different
sets of acoustic patterns automatically discovered on different points properly
distributed over this three-dimensional space are complementary to one another,
thus can jointly capture the characteristics of the spoken terms. By
representing the spoken content and spoken query as sequences of acoustic
patterns, a series of approaches for matching the pattern index sequences while
considering the signal variations are developed. In this way, not only the
on-line computation load can be reduced, but the signal distributions caused by
different speakers and acoustic conditions can be reasonably taken care of. The
results indicate that this approach significantly outperformed the unsupervised
feature-based DTW baseline by 16.16\% in mean average precision on the TIMIT
corpus.
Unsupervised Discovery of Linguistic Structure Including Two-level Acoustic Patterns Using Three Cascaded Stages of Iterative Optimization
Authors: Cheng-Tao Chung, Chun-an Chan, Lin-shan Lee
Date: 2015-09-07 00:00:00
Tasks: Language Modeling, Language Modelling

Techniques for unsupervised discovery of acoustic patterns are getting
increasingly attractive, because huge quantities of speech data are becoming
available but manual annotations remain hard to acquire. In this paper, we
propose an approach for unsupervised discovery of linguistic structure for the
target spoken language given raw speech data. This linguistic structure
includes two-level (subword-like and word-like) acoustic patterns, the lexicon
of word-like patterns in terms of subword-like patterns and the N-gram language
model based on word-like patterns. All patterns, models, and parameters can be
automatically learned from the unlabelled speech corpus. This is achieved by an
initialization step followed by three cascaded stages for acoustic, linguistic,
and lexical iterative optimization. The lexicon of word-like patterns defines
allowed consecutive sequence of HMMs for subword-like patterns. In each
iteration, model training and decoding produces updated labels from which the
lexicon and HMMs can be further updated. In this way, model parameters and
decoded labels are respectively optimized in each iteration, and the knowledge
about the linguistic structure is learned gradually layer after layer. The
proposed approach was tested in preliminary experiments on a corpus of Mandarin
broadcast news, including a task of spoken term detection with performance
compared to a parallel test using models trained in a supervised way. Results
show that the proposed system not only yields reasonable performance on its
own, but is also complimentary to existing large vocabulary ASR systems.
VESICLE: Volumetric Evaluation of Synaptic Interfaces using Computer vision at Large Scale
Authors: William Gray Roncal, Michael Pekala, Verena Kaynig-Fittkau, Dean M. Kleissas, Joshua T. Vogelstein, Hanspeter Pfister, Randal Burns, R. Jacob Vogelstein, Mark A. Chevillet, Gregory D. Hager
Date: 2014-03-14 00:00:00
Tasks: object-detection, Object Detection

An open challenge problem at the forefront of modern neuroscience is to
obtain a comprehensive mapping of the neural pathways that underlie human brain
function; an enhanced understanding of the wiring diagram of the brain promises
to lead to new breakthroughs in diagnosing and treating neurological disorders.
Inferring brain structure from image data, such as that obtained via electron
microscopy (EM), entails solving the problem of identifying biological
structures in large data volumes. Synapses, which are a key communication
structure in the brain, are particularly difficult to detect due to their small
size and limited contrast. Prior work in automated synapse detection has relied
upon time-intensive biological preparations (post-staining, isotropic slice
thicknesses) in order to simplify the problem.
  This paper presents VESICLE, the first known approach designed for mammalian
synapse detection in anisotropic, non-post-stained data. Our methods explicitly
leverage biological context, and the results exceed existing synapse detection
methods in terms of accuracy and scalability. We provide two different
approaches - one a deep learning classifier (VESICLE-CNN) and one a lightweight
Random Forest approach (VESICLE-RF) to offer alternatives in the
performance-scalability space. Addressing this synapse detection challenge
enables the analysis of high-throughput imaging data soon expected to reach
petabytes of data, and provide tools for more rapid estimation of brain-graphs.
Finally, to facilitate community efforts, we developed tools for large-scale
object detection, and demonstrated this framework to find $\approx$ 50,000
synapses in 60,000 $\mu m ^3$ (220 GB on disk) of electron microscopy data.
MegaFace: A Million Faces for Recognition at Scale
Authors: D. Miller, E. Brossard, S. Seitz, I. Kemelmacher-Shlizerman
Date: 2015-05-08 00:00:00
Tasks: Face Recognition

Recent face recognition experiments on the LFW benchmark show that face
recognition is performing stunningly well, surpassing human recognition rates.
In this paper, we study face recognition at scale. Specifically, we have
collected from Flickr a \textbf{Million} faces and evaluated state of the art
face recognition algorithms on this dataset. We found that the performance of
algorithms varies--while all perform great on LFW, once evaluated at scale
recognition rates drop drastically for most algorithms. Interestingly, deep
learning based approach by \cite{schroff2015facenet} performs much better, but
still gets less robust at scale. We consider both verification and
identification problems, and evaluate how pose affects recognition at scale.
Moreover, we ran an extensive human study on Mechanical Turk to evaluate human
recognition at scale, and report results. All the photos are creative commons
photos and is released at \small{\url{http://megaface.cs.washington.edu/}} for
research and further experiments.
Structured Prediction with Output Embeddings for Semantic Image Annotation
Authors: Ariadna Quattoni, Arnau Ramisa, Pranava Swaroop Madhyastha, Edgar Simo-Serra, Francesc Moreno-Noguer
Date: 2015-09-07 00:00:00
Proceeding: NAACL 2016 6
Tasks: Prediction, Structured Prediction

We address the task of annotating images with semantic tuples. Solving this
problem requires an algorithm which is able to deal with hundreds of classes
for each argument of the tuple. In such contexts, data sparsity becomes a key
challenge, as there will be a large number of classes for which only a few
examples are available. We propose handling this by incorporating feature
representations of both the inputs (images) and outputs (argument classes) into
a factorized log-linear model, and exploiting the flexibility of scoring
functions based on bilinear forms. Experiments show that integrating feature
representations of the outputs in the structured prediction model leads to
better overall predictions. We also conclude that the best output
representation is specific for each type of argument.
Convexity Shape Constraints for Image Segmentation
Authors: Loic A. Royer, David L. Richmond, Carsten Rother, Bjoern Andres, Dagmar Kainmueller
Date: 2015-09-07 00:00:00
Proceeding: CVPR 2016 6
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Segmenting an image into multiple components is a central task in computer
vision. In many practical scenarios, prior knowledge about plausible components
is available. Incorporating such prior knowledge into models and algorithms for
image segmentation is highly desirable, yet can be non-trivial. In this work,
we introduce a new approach that allows, for the first time, to constrain some
or all components of a segmentation to have convex shapes. Specifically, we
extend the Minimum Cost Multicut Problem by a class of constraints that enforce
convexity. To solve instances of this APX-hard integer linear program to
optimality, we separate the proposed constraints in the branch-and-cut loop of
a state-of-the-art ILP solver. Results on natural and biological images
demonstrate the effectiveness of the approach as well as its advantage over the
state-of-the-art heuristic.
Future Localization from an Egocentric Depth Image
Authors: Hyun Soo Park, Yedong Niu, Jianbo Shi
Date: 2015-09-07 00:00:00
Tasks: object-detection, Object Detection

This paper presents a method for future localization: to predict a set of
plausible trajectories of ego-motion given a depth image. We predict paths
avoiding obstacles, between objects, even paths turning around a corner into
space behind objects. As a byproduct of the predicted trajectories of
ego-motion, we discover in the image the empty space occluded by foreground
objects. We use no image based features such as semantic labeling/segmentation
or object detection/recognition for this algorithm. Inspired by proxemics, we
represent the space around a person using an EgoSpace map, akin to an
illustrated tourist map, that measures a likelihood of occlusion at the
egocentric coordinate system. A future trajectory of ego-motion is modeled by a
linear combination of compact trajectory bases allowing us to constrain the
predicted trajectory. We learn the relationship between the EgoSpace map and
trajectory from the EgoMotion dataset providing in-situ measurements of the
future trajectory. A cost function that takes into account partial occlusion
due to foreground objects is minimized to predict a trajectory. This cost
function generates a trajectory that passes through the occluded space, which
allows us to discover the empty space behind the foreground objects. We
quantitatively evaluate our method to show predictive validity and apply to
various real world scenes including walking, shopping, and social interactions.
Matrix Factorisation with Linear Filters
Authors: Ömer Deniz Akyildiz
Date: 2015-09-07 00:00:00
Tasks: Image Restoration

This text investigates relations between two well-known family of algorithms,
matrix factorisations and recursive linear filters, by describing a
probabilistic model in which approximate inference corresponds to a matrix
factorisation algorithm. Using the probabilistic model, we derive a matrix
factorisation algorithm as a recursive linear filter. More precisely, we derive
a matrix-variate recursive linear filter in order to perform efficient
inference in high dimensions. We also show that it is possible to interpret our
algorithm as a nontrivial stochastic gradient algorithm. Demonstrations and
comparisons on an image restoration task are given.
An Infra-Structure for Performance Estimation and Experimental Comparison of Predictive Models in R
Authors: Luis Torgo
Date: 2014-12-01 00:00:00
Tasks: General Classification, regression, Time Series, Time Series Analysis

This document describes an infra-structure provided by the R package
performanceEstimation that allows to estimate the predictive performance of
different approaches (workflows) to predictive tasks. The infra-structure is
generic in the sense that it can be used to estimate the values of any
performance metrics, for any workflow on different predictive tasks, namely,
classification, regression and time series tasks. The package also includes
several standard workflows that allow users to easily set up their experiments
limiting the amount of work and information they need to provide. The overall
goal of the infra-structure provided by our package is to facilitate the task
of estimating the predictive performance of different modeling approaches to
predictive tasks in the R environment.
A New Low-Rank Tensor Model for Video Completion
Authors: Wenrui Hu, DaCheng Tao, Wensheng Zhang, Yuan Xie, Yehui Yang
Date: 2015-09-07 00:00:00
Tasks: Missing Values

In this paper, we propose a new low-rank tensor model based on the circulant
algebra, namely, twist tensor nuclear norm or t-TNN for short. The twist tensor
denotes a 3-way tensor representation to laterally store 2D data slices in
order. On one hand, t-TNN convexly relaxes the tensor multi-rank of the twist
tensor in the Fourier domain, which allows an efficient computation using FFT.
On the other, t-TNN is equal to the nuclear norm of block circulant
matricization of the twist tensor in the original domain, which extends the
traditional matrix nuclear norm in a block circulant way. We test the t-TNN
model on a video completion application that aims to fill missing values and
the experiment results validate its effectiveness, especially when dealing with
video recorded by a non-stationary panning camera. The block circulant
matricization of the twist tensor can be transformed into a circulant block
representation with nuclear norm invariance. This representation, after
transformation, exploits the horizontal translation relationship between the
frames in a video, and endows the t-TNN model with a more powerful ability to
reconstruct panning videos than the existing state-of-the-art low-rank models.