1,876 research outputs found
Text to Multi-level MindMaps: A Novel Method for Hierarchical Visual Abstraction of Natural Language Text
MindMapping is a well-known technique used in note taking, which encourages
learning and studying. MindMapping has been manually adopted to help present
knowledge and concepts in a visual form. Unfortunately, there is no reliable
automated approach to generate MindMaps from Natural Language text. This work
firstly introduces MindMap Multilevel Visualization concept which is to jointly
visualize and summarize textual information. The visualization is achieved
pictorially across multiple levels using semantic information (i.e. ontology),
while the summarization is achieved by the information in the highest levels as
they represent abstract information in the text. This work also presents the
first automated approach that takes a text input and generates a MindMap
visualization out of it. The approach could visualize text documents in
multilevel MindMaps, in which a high-level MindMap node could be expanded into
child MindMaps. \ignore{ As far as we know, this is the first work that view
MindMapping as a new approach to jointly summarize and visualize textual
information.} The proposed method involves understanding of the input text and
converting it into intermediate Detailed Meaning Representation (DMR). The DMR
is then visualized with two modes; Single level or Multiple levels, which is
convenient for larger text. The generated MindMaps from both approaches were
evaluated based on Human Subject experiments performed on Amazon Mechanical
Turk with various parameter settings.Comment: 31 page
Generalized Twin Gaussian Processes using Sharma-Mittal Divergence
There has been a growing interest in mutual information measures due to their
wide range of applications in Machine Learning and Computer Vision. In this
paper, we present a generalized structured regression framework based on
Shama-Mittal divergence, a relative entropy measure, which is introduced to the
Machine Learning community in this work. Sharma-Mittal (SM) divergence is a
generalized mutual information measure for the widely used R\'enyi, Tsallis,
Bhattacharyya, and Kullback-Leibler (KL) relative entropies. Specifically, we
study Sharma-Mittal divergence as a cost function in the context of the Twin
Gaussian Processes (TGP)~\citep{Bo:2010}, which generalizes over the
KL-divergence without computational penalty. We show interesting properties of
Sharma-Mittal TGP (SMTGP) through a theoretical analysis, which covers missing
insights in the traditional TGP formulation. However, we generalize this theory
based on SM-divergence instead of KL-divergence which is a special case.
Experimentally, we evaluated the proposed SMTGP framework on several datasets.
The results show that SMTGP reaches better predictions than KL-based TGP, since
it offers a bigger class of models through its parameters that we learn from
the data.Comment: This work got accepted for Publication in the Machine Learning
Journal 2015. The work is scheduled for presentation at ECML-PKDD 2015
journal track paper
Quantifying Creativity in Art Networks
Can we develop a computer algorithm that assesses the creativity of a
painting given its context within art history? This paper proposes a novel
computational framework for assessing the creativity of creative products, such
as paintings, sculptures, poetry, etc. We use the most common definition of
creativity, which emphasizes the originality of the product and its influential
value. The proposed computational framework is based on constructing a network
between creative products and using this network to infer about the originality
and influence of its nodes. Through a series of transformations, we construct a
Creativity Implication Network. We show that inference about creativity in this
network reduces to a variant of network centrality problems which can be solved
efficiently. We apply the proposed framework to the task of quantifying
creativity of paintings (and sculptures). We experimented on two datasets with
over 62K paintings to illustrate the behavior of the proposed framework. We
also propose a methodology for quantitatively validating the results of the
proposed algorithm, which we call the "time machine experiment".Comment: This paper will be published in the sixth International Conference on
Computational Creativity (ICCC) June 29-July 2nd 2015, Park City, Utah, USA.
This arXiv version is an extended version of the conference pape
A Multilayer-Based Framework for Online Background Subtraction with Freely Moving Cameras
The exponentially increasing use of moving platforms for video capture
introduces the urgent need to develop the general background subtraction
algorithms with the capability to deal with the moving background. In this
paper, we propose a multilayer-based framework for online background
subtraction for videos captured by moving cameras. Unlike the previous
treatments of the problem, the proposed method is not restricted to binary
segmentation of background and foreground, but formulates it as a multi-label
segmentation problem by modeling multiple foreground objects in different
layers when they appear simultaneously in the scene. We assign an independent
processing layer to each foreground object, as well as the background, where
both motion and appearance models are estimated, and a probability map is
inferred using a Bayesian filtering framework. Finally, Multi-label Graph-cut
on Markov Random Field is employed to perform pixel-wise labeling. Extensive
evaluation results show that the proposed method outperforms state-of-the-art
methods on challenging video sequences.Comment: Accepted by ICCV'1
DISCOMAX: A Proximity-Preserving Distance Correlation Maximization Algorithm
In a regression setting we propose algorithms that reduce the dimensionality
of the features while simultaneously maximizing a statistical measure of
dependence known as distance correlation between the low-dimensional features
and a response variable. This helps in solving the prediction problem with a
low-dimensional set of features. Our setting is different from subset-selection
algorithms where the problem is to choose the best subset of features for
regression. Instead, we attempt to generate a new set of low-dimensional
features as in a feature-learning setting. We attempt to keep our proposed
approach as model-free and our algorithm does not assume the application of any
specific regression model in conjunction with the low-dimensional features that
it learns. The algorithm is iterative and is fomulated as a combination of the
majorization-minimization and concave-convex optimization procedures. We also
present spectral radius based convergence results for the proposed iterations.Comment: Withdrawing as an updated and enhanced version of this paper is on
arxiv under my name as well titled Supervised Dimensionality Reduction via
Distance Correlation Maximization. See arXiv:1601.00236. That makes this
version pointles
Learning Kernels for Structured Prediction using Polynomial Kernel Transformations
Learning the kernel functions used in kernel methods has been a vastly
explored area in machine learning. It is now widely accepted that to obtain
'good' performance, learning a kernel function is the key challenge. In this
work we focus on learning kernel representations for structured regression. We
propose use of polynomials expansion of kernels, referred to as Schoenberg
transforms and Gegenbaur transforms, which arise from the seminal result of
Schoenberg (1938). These kernels can be thought of as polynomial combination of
input features in a high dimensional reproducing kernel Hilbert space (RKHS).
We learn kernels over input and output for structured data, such that,
dependency between kernel features is maximized. We use Hilbert-Schmidt
Independence Criterion (HSIC) to measure this. We also give an efficient,
matrix decomposition-based algorithm to learn these kernel transformations, and
demonstrate state-of-the-art results on several real-world datasets
Large-scale Classification of Fine-Art Paintings: Learning The Right Metric on The Right Feature
In the past few years, the number of fine-art collections that are digitized
and publicly available has been growing rapidly. With the availability of such
large collections of digitized artworks comes the need to develop multimedia
systems to archive and retrieve this pool of data. Measuring the visual
similarity between artistic items is an essential step for such multimedia
systems, which can benefit more high-level multimedia tasks. In order to model
this similarity between paintings, we should extract the appropriate visual
features for paintings and find out the best approach to learn the similarity
metric based on these features. We investigate a comprehensive list of visual
features and metric learning approaches to learn an optimized similarity
measure between paintings. We develop a machine that is able to make
aesthetic-related semantic-level judgments, such as predicting a painting's
style, genre, and artist, as well as providing similarity measures optimized
based on the knowledge available in the domain of art historical
interpretation. Our experiments show the value of using this similarity measure
for the aforementioned prediction tasks.Comment: 21 page
Visual-Semantic Scene Understanding by Sharing Labels in a Context Network
We consider the problem of naming objects in complex, natural scenes
containing widely varying object appearance and subtly different names.
Informed by cognitive research, we propose an approach based on sharing context
based object hypotheses between visual and lexical spaces. To this end, we
present the Visual Semantic Integration Model (VSIM) that represents object
labels as entities shared between semantic and visual contexts and infers a new
image by updating labels through context switching. At the core of VSIM is a
semantic Pachinko Allocation Model and a visual nearest neighbor Latent
Dirichlet Allocation Model. For inference, we derive an iterative Data
Augmentation algorithm that pools the label probabilities and maximizes the
joint label posterior of an image. Our model surpasses the performance of
state-of-art methods in several visual tasks on the challenging SUN09 dataset
Manifold-Kernels Comparison in MKPLS for Visual Speech Recognition
Speech recognition is a challenging problem. Due to the acoustic limitations,
using visual information is essential for improving the recognition accuracy in
real-life unconstraint situations. One common approach is to model the visual
recognition as nonlinear optimization problem. Measuring the distances between
visual units is essential for solving this problem. Embedding the visual units
on a manifold and using manifold kernels is one way to measure these distances.
This work is intended to evaluate the performance of several manifold kernels
for solving the problem of visual speech recognition. We show the theory behind
each kernel. We apply manifold kernel partial least squares framework to OuluVs
and AvLetters databases, and show empirical comparison between all kernels.
This framework provides convenient way to explore different kernels
The Digital Humanities Unveiled: Perceptions Held by Art Historians and Computer Scientists about Computer Vision Technology
Although computer scientists are generally familiar with the achievements of
computer vision technology in art history, these accomplishments are little
known and often misunderstood by scholars in the humanities. To clarify the
parameters of this seeming disjuncture, we have addressed the concerns that one
example of the digitization of the humanities poses on social, philosophical,
and practical levels. In support of our assessment of the perceptions held by
computer scientists and art historians about the use of computer vision
technology to examine art, we based our interpretations on two surveys that
were distributed in August 2014. In this paper, the development of these
surveys and their results are discussed in the context of the major
philosophical conclusions of our research in this area to date.Comment: arXiv admin note: substantial text overlap with arXiv:1410.248
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