pwc-archive/papers-with-abstracts huggingface dataset:
An Elastic Image Registration Approach for Wireless Capsule Endoscope Localization
Authors: Isabel N. Figueiredo, Carlos Leal, Luís Pinto, Pedro N. Figueiredo, Richard Tsai
Date: 2015-04-23 00:00:00
Tasks: Affine Image Registration, Image Registration

Wireless Capsule Endoscope (WCE) is an innovative imaging device that permits
physicians to examine all the areas of the Gastrointestinal (GI) tract. It is
especially important for the small intestine, where traditional invasive
endoscopies cannot reach. Although WCE represents an extremely important
advance in medical imaging, a major drawback that remains unsolved is the WCE
precise location in the human body during its operating time. This is mainly
due to the complex physiological environment and the inherent capsule effects
during its movement. When an abnormality is detected, in the WCE images,
medical doctors do not know precisely where this abnormality is located
relative to the intestine and therefore they can not proceed efficiently with
the appropriate therapy. The primary objective of the present paper is to give
a contribution to WCE localization, using image-based methods. The main focus
of this work is on the description of a multiscale elastic image registration
approach, its experimental application on WCE videos, and comparison with a
multiscale affine registration. The proposed approach includes registrations
that capture both rigid-like and non-rigid deformations, due respectively to
the rigid-like WCE movement and the elastic deformation of the small intestine
originated by the GI peristaltic movement. Under this approach a qualitative
information about the WCE speed can be obtained, as well as the WCE location
and orientation via projective geometry. The results of the experimental tests
with real WCE video frames show the good performance of the proposed approach,
when elastic deformations of the small intestine are involved in successive
frames, and its superiority with respect to a multiscale affine image
registration, which accounts for rigid-like deformations only and discards
elastic deformations.
DeepEdge: A Multi-Scale Bifurcated Deep Network for Top-Down Contour Detection
Authors: Gedas Bertasius, Jianbo Shi, Lorenzo Torresani
Date: 2014-12-02 00:00:00
Proceeding: CVPR 2015 6
Tasks: Contour Detection, Feature Engineering, General Classification, Image Segmentation, Object, object-detection, Object Detection, Semantic Segmentation

Contour detection has been a fundamental component in many image segmentation
and object detection systems. Most previous work utilizes low-level features
such as texture or saliency to detect contours and then use them as cues for a
higher-level task such as object detection. However, we claim that recognizing
objects and predicting contours are two mutually related tasks. Contrary to
traditional approaches, we show that we can invert the commonly established
pipeline: instead of detecting contours with low-level cues for a higher-level
recognition task, we exploit object-related features as high-level cues for
contour detection.
  We achieve this goal by means of a multi-scale deep network that consists of
five convolutional layers and a bifurcated fully-connected sub-network. The
section from the input layer to the fifth convolutional layer is fixed and
directly lifted from a pre-trained network optimized over a large-scale object
classification task. This section of the network is applied to four different
scales of the image input. These four parallel and identical streams are then
attached to a bifurcated sub-network consisting of two independently-trained
branches. One branch learns to predict the contour likelihood (with a
classification objective) whereas the other branch is trained to learn the
fraction of human labelers agreeing about the contour presence at a given point
(with a regression criterion).
  We show that without any feature engineering our multi-scale deep learning
approach achieves state-of-the-art results in contour detection.
Collectively Embedding Multi-Relational Data for Predicting User Preferences
Authors: Nitish Gupta, Sameer Singh
Date: 2015-04-23 00:00:00
Tasks: All, Attribute, Collaborative Filtering

Matrix factorization has found incredible success and widespread application
as a collaborative filtering based approach to recommendations. Unfortunately,
incorporating additional sources of evidence, especially ones that are
incomplete and noisy, is quite difficult to achieve in such models, however, is
often crucial for obtaining further gains in accuracy. For example, additional
information about businesses from reviews, categories, and attributes should be
leveraged for predicting user preferences, even though this information is
often inaccurate and partially-observed. Instead of creating customized methods
that are specific to each type of evidences, in this paper we present a generic
approach to factorization of relational data that collectively models all the
relations in the database. By learning a set of embeddings that are shared
across all the relations, the model is able to incorporate observed information
from all the relations, while also predicting all the relations of interest.
Our evaluation on multiple Amazon and Yelp datasets demonstrates effective
utilization of additional information for held-out preference prediction, but
further, we present accurate models even for the cold-starting businesses and
products for which we do not observe any ratings or reviews. We also illustrate
the capability of the model in imputing missing information and jointly
visualizing words, categories, and attribute factors.
From End-User's Requirements to Web Services Retrieval: A Semantic and Intention-Driven Approach
Authors: Isabelle Mirbel, Pierre Crescenzo
Date: 2015-04-23 00:00:00
Tasks: Retrieval

In this paper, we present SATIS, a framework to derive Web Service
specifications from end-user's requirements in order to opera-tionalise
business processes in the context of a specific application domain. The aim of
SATIS is to provide to neuroscientists, which are not familiar with computer
science, a complete solution to easily find a set of Web Services to implement
an image processing pipeline. More precisely, our framework offers the
capability to capture high-level end-user's requirements in an iterative and
incremental way and to turn them into queries to retrieve Web Services
description. The whole framework relies on reusable and combinable elements
which can be shared out by a community of users sharing some interest or
problems for a given topic. In our approach, we adopt Web semantic languages
and models as a unified framework to deal with end-user's requirements and Web
Service descriptions in order to take advantage of their reasoning and
traceability capabilities.
Robust Principal Component Analysis on Graphs
Authors: Nauman Shahid, Vassilis Kalofolias, Xavier Bresson, Michael Bronstein, Pierre Vandergheynst
Date: 2015-04-23 00:00:00
Proceeding: ICCV 2015 12
Tasks: Clustering, Dimensionality Reduction, Missing Values

Principal Component Analysis (PCA) is the most widely used tool for linear
dimensionality reduction and clustering. Still it is highly sensitive to
outliers and does not scale well with respect to the number of data samples.
Robust PCA solves the first issue with a sparse penalty term. The second issue
can be handled with the matrix factorization model, which is however
non-convex. Besides, PCA based clustering can also be enhanced by using a graph
of data similarity. In this article, we introduce a new model called "Robust
PCA on Graphs" which incorporates spectral graph regularization into the Robust
PCA framework. Our proposed model benefits from 1) the robustness of principal
components to occlusions and missing values, 2) enhanced low-rank recovery, 3)
improved clustering property due to the graph smoothness assumption on the
low-rank matrix, and 4) convexity of the resulting optimization problem.
Extensive experiments on 8 benchmark, 3 video and 2 artificial datasets with
corruptions clearly reveal that our model outperforms 10 other state-of-the-art
models in its clustering and low-rank recovery tasks.
Sparse Radial Sampling LBP for Writer Identification
Authors: Anguelos Nicolaou, Andrew D. Bagdanov, Marcus Liwicki, Dimosthenis Karatzas
Date: 2015-04-23 00:00:00
Tasks: Binarization, General Classification, Texture Classification

In this paper we present the use of Sparse Radial Sampling Local Binary
Patterns, a variant of Local Binary Patterns (LBP) for text-as-texture
classification. By adapting and extending the standard LBP operator to the
particularities of text we get a generic text-as-texture classification scheme
and apply it to writer identification. In experiments on CVL and ICDAR 2013
datasets, the proposed feature-set demonstrates State-Of-the-Art (SOA)
performance. Among the SOA, the proposed method is the only one that is based
on dense extraction of a single local feature descriptor. This makes it fast
and applicable at the earliest stages in a DIA pipeline without the need for
segmentation, binarization, or extraction of multiple features.
Unsupervised Feature Learning for Dense Correspondences across Scenes
Authors: Chao Zhang, Chunhua Shen, Tingzhi Shen
Date: 2015-01-04 00:00:00
Tasks: Dictionary Learning

We propose a fast, accurate matching method for estimating dense pixel
correspondences across scenes. It is a challenging problem to estimate dense
pixel correspondences between images depicting different scenes or instances of
the same object category. While most such matching methods rely on hand-crafted
features such as SIFT, we learn features from a large amount of unlabeled image
patches using unsupervised learning. Pixel-layer features are obtained by
encoding over the dictionary, followed by spatial pooling to obtain patch-layer
features. The learned features are then seamlessly embedded into a multi-layer
match- ing framework. We experimentally demonstrate that the learned features,
together with our matching model, outperforms state-of-the-art methods such as
the SIFT flow, coherency sensitive hashing and the recent deformable spatial
pyramid matching methods both in terms of accuracy and computation efficiency.
Furthermore, we evaluate the performance of a few different dictionary learning
and feature encoding methods in the proposed pixel correspondences estimation
framework, and analyse the impact of dictionary learning and feature encoding
with respect to the final matching performance.
svcR: An R Package for Support Vector Clustering improved with Geometric Hashing applied to Lexical Pattern Discovery
Authors: Nicolas Turenne
Date: 2015-04-23 00:00:00
Tasks: Clustering

We present a new R package which takes a numerical matrix format as data
input, and computes clusters using a support vector clustering method (SVC). We
have implemented an original 2D-grid labeling approach to speed up cluster
extraction. In this sense, SVC can be seen as an efficient cluster extraction
if clusters are separable in a 2-D map. Secondly we showed that this SVC
approach using a Jaccard-Radial base kernel can help to classify well enough a
set of terms into ontological classes and help to define regular expression
rules for information extraction in documents; our case study concerns a set of
terms and documents about developmental and molecular biology.
x.ent: R Package for Entities and Relations Extraction based on Unsupervised Learning and Document Structure
Authors: Nicolas Turenne, Tien Phan
Date: 2015-04-23 00:00:00
Tasks: Epidemiology, Relation, Relation Extraction

Relation extraction with accurate precision is still a challenge when
processing full text databases. We propose an approach based on cooccurrence
analysis in each document for which we used document organization to improve
accuracy of relation extraction. This approach is implemented in a R package
called \emph{x.ent}. Another facet of extraction relies on use of extracted
relation into a querying system for expert end-users. Two datasets had been
used. One of them gets interest from specialists of epidemiology in plant
health. For this dataset usage is dedicated to plant-disease exploration
through agricultural information news. An open-data platform exploits exports
from \emph{x.ent} and is publicly available.
Open Data Platform for Knowledge Access in Plant Health Domain : VESPA Mining
Authors: Nicolas Turenne, Mathieu Andro, Roselyne Corbière, Tien T. Phan
Date: 2015-04-23 00:00:00

Important data are locked in ancient literature. It would be uneconomic to
produce these data again and today or to extract them without the help of text
mining technologies. Vespa is a text mining project whose aim is to extract
data on pest and crops interactions, to model and predict attacks on crops, and
to reduce the use of pesticides. A few attempts proposed an agricultural
information access. Another originality of our work is to parse documents with
a dependency of the document architecture.
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
Authors: Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Date: 2014-06-18 00:00:00
Tasks: General Classification, image-classification, Image Classification, object-detection, Object Detection, Object Recognition

Existing deep convolutional neural networks (CNNs) require a fixed-size
(e.g., 224x224) input image. This requirement is "artificial" and may reduce
the recognition accuracy for the images or sub-images of an arbitrary
size/scale. In this work, we equip the networks with another pooling strategy,
"spatial pyramid pooling", to eliminate the above requirement. The new network
structure, called SPP-net, can generate a fixed-length representation
regardless of image size/scale. Pyramid pooling is also robust to object
deformations. With these advantages, SPP-net should in general improve all
CNN-based image classification methods. On the ImageNet 2012 dataset, we
demonstrate that SPP-net boosts the accuracy of a variety of CNN architectures
despite their different designs. On the Pascal VOC 2007 and Caltech101
datasets, SPP-net achieves state-of-the-art classification results using a
single full-image representation and no fine-tuning.
  The power of SPP-net is also significant in object detection. Using SPP-net,
we compute the feature maps from the entire image only once, and then pool
features in arbitrary regions (sub-images) to generate fixed-length
representations for training the detectors. This method avoids repeatedly
computing the convolutional features. In processing test images, our method is
24-102x faster than the R-CNN method, while achieving better or comparable
accuracy on Pascal VOC 2007.
  In ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2014, our
methods rank #2 in object detection and #3 in image classification among all 38
teams. This manuscript also introduces the improvement made for this
competition.
Understanding and Diagnosing Visual Tracking Systems
Authors: Naiyan Wang, Jianping Shi, Dit-yan Yeung, Jiaya Jia
Date: 2015-04-23 00:00:00
Proceeding: ICCV 2015 12
Tasks: Visual Tracking

Several benchmark datasets for visual tracking research have been proposed in
recent years. Despite their usefulness, whether they are sufficient for
understanding and diagnosing the strengths and weaknesses of different trackers
remains questionable. To address this issue, we propose a framework by breaking
a tracker down into five constituent parts, namely, motion model, feature
extractor, observation model, model updater, and ensemble post-processor. We
then conduct ablative experiments on each component to study how it affects the
overall result. Surprisingly, our findings are discrepant with some common
beliefs in the visual tracking research community. We find that the feature
extractor plays the most important role in a tracker. On the other hand,
although the observation model is the focus of many studies, we find that it
often brings no significant improvement. Moreover, the motion model and model
updater contain many details that could affect the result. Also, the ensemble
post-processor can improve the result substantially when the constituent
trackers have high diversity. Based on our findings, we put together some very
elementary building blocks to give a basic tracker which is competitive in
performance to the state-of-the-art trackers. We believe our framework can
provide a solid baseline when conducting controlled experiments for visual
tracking research.
Transferring Rich Feature Hierarchies for Robust Visual Tracking
Authors: Naiyan Wang, Siyi Li, Abhinav Gupta, Dit-yan Yeung
Date: 2015-01-19 00:00:00
Tasks: image-classification, Image Classification, Object, object-detection, Object Detection, Object Tracking, Visual Tracking

Convolutional neural network (CNN) models have demonstrated great success in
various computer vision tasks including image classification and object
detection. However, some equally important tasks such as visual tracking remain
relatively unexplored. We believe that a major hurdle that hinders the
application of CNN to visual tracking is the lack of properly labeled training
data. While existing applications that liberate the power of CNN often need an
enormous amount of training data in the order of millions, visual tracking
applications typically have only one labeled example in the first frame of each
video. We address this research issue here by pre-training a CNN offline and
then transferring the rich feature hierarchies learned to online tracking. The
CNN is also fine-tuned during online tracking to adapt to the appearance of the
tracked target specified in the first video frame. To fit the characteristics
of object tracking, we first pre-train the CNN to recognize what is an object,
and then propose to generate a probability map instead of producing a simple
class label. Using two challenging open benchmarks for performance evaluation,
our proposed tracker has demonstrated substantial improvement over other
state-of-the-art trackers.
Edge Detection Based on Global and Local Parameters of the Image
Authors: Andrew F. C. Brustolin
Date: 2015-04-23 00:00:00
Tasks: Edge Detection

This paper presents an edge detection method based on global and local
parameters of the image, which produces satisfactory results on the edge
detection of complex images and has a simple structure for execution. The local
and global parameters of the image are arithmetic means and standard
deviations, the former acquired from a three sized window representing five
pixels, the latter acquired from the entire row or column. We obtain the
differences of grayscale intensities between two adjacent pixels and the sum of
the modulus of these differences from the horizontal and vertical scans of the
image. Using these obtained values, we calculate the local and global
parameters. After the gathering of the local and global parameters, we compare
each sum of the modulus of differences with its own local and global parameter.
In the case of the comparison is true, the consecutive pixel to the modulus sum
of differences index is marked as an edge. We present the results of the tests
with grayscale images using different parameters and discuss the advantages and
disadvantages of each parameter value and algorithm structure chosen on the
edge processing. There is a comparison of results between this papers detector
and Canny, where we evaluate the quality of the presented detector.
Review Mining for Feature Based Opinion Summarization and Visualization
Authors: Ahmad Kamal
Date: 2015-04-13 00:00:00
Tasks: Marketing, Opinion Mining, Opinion Summarization, Product Recommendation, Sentence, Sentiment Analysis

The application and usage of opinion mining, especially for business
intelligence, product recommendation, targeted marketing etc. have fascinated
many research attentions around the globe. Various research efforts attempted
to mine opinions from customer reviews at different levels of granularity,
including word-, sentence-, and document-level. However, development of a fully
automatic opinion mining and sentiment analysis system is still elusive. Though
the development of opinion mining and sentiment analysis systems are getting
momentum, most of them attempt to perform document-level sentiment analysis,
classifying a review document as positive, negative, or neutral. Such
document-level opinion mining approaches fail to provide insight about users
sentiment on individual features of a product or service. Therefore, it seems
to be a great help for both customers and manufacturers, if the reviews could
be processed at a finer-grained level and presented in a summarized form
through some visual means, highlighting individual features of a product and
users sentiment expressed over them. In this paper, the design of a unified
opinion mining and sentiment analysis framework is presented at the
intersection of both machine learning and natural language processing
approaches. Also, design of a novel feature-level review summarization scheme
is proposed to visualize mined features, opinions and their polarity values in
a comprehendible way.
Graphical Fermat's Principle and Triangle-Free Graph Estimation
Authors: Junwei Lu, Han Liu
Date: 2015-04-23 00:00:00

We consider the problem of estimating undirected triangle-free graphs of high
dimensional distributions. Triangle-free graphs form a rich graph family which
allows arbitrary loopy structures but 3-cliques. For inferential tractability,
we propose a graphical Fermat's principle to regularize the distribution
family. Such principle enforces the existence of a distribution-dependent
pseudo-metric such that any two nodes have a smaller distance than that of two
other nodes who have a geodesic path include these two nodes. Guided by this
principle, we show that a greedy strategy is able to recover the true graph.
The resulting algorithm only requires a pairwise distance matrix as input and
is computationally even more efficient than calculating the minimum spanning
tree. We consider graph estimation problems under different settings, including
discrete and nonparametric distribution families. Thorough numerical results
are provided to illustrate the usefulness of the proposed method.
Deep Spatial Pyramid: The Devil is Once Again in the Details
Authors: Bin-Bin Gao, Xiu-Shen Wei, Jianxin Wu, Weiyao Lin
Date: 2015-04-21 00:00:00
Tasks: General Classification, image-classification, Image Classification

In this paper we show that by carefully making good choices for various
detailed but important factors in a visual recognition framework using deep
learning features, one can achieve a simple, efficient, yet highly accurate
image classification system. We first list 5 important factors, based on both
existing researches and ideas proposed in this paper. These important detailed
factors include: 1) $\ell_2$ matrix normalization is more effective than
unnormalized or $\ell_2$ vector normalization, 2) the proposed natural deep
spatial pyramid is very effective, and 3) a very small $K$ in Fisher Vectors
surprisingly achieves higher accuracy than normally used large $K$ values.
Along with other choices (convolutional activations and multiple scales), the
proposed DSP framework is not only intuitive and efficient, but also achieves
excellent classification accuracy on many benchmark datasets. For example,
DSP's accuracy on SUN397 is 59.78%, significantly higher than previous
state-of-the-art (53.86%).