8,516 research outputs found
Feature and Region Selection for Visual Learning
Visual learning problems such as object classification and action recognition
are typically approached using extensions of the popular bag-of-words (BoW)
model. Despite its great success, it is unclear what visual features the BoW
model is learning: Which regions in the image or video are used to discriminate
among classes? Which are the most discriminative visual words? Answering these
questions is fundamental for understanding existing BoW models and inspiring
better models for visual recognition.
To answer these questions, this paper presents a method for feature selection
and region selection in the visual BoW model. This allows for an intermediate
visualization of the features and regions that are important for visual
learning. The main idea is to assign latent weights to the features or regions,
and jointly optimize these latent variables with the parameters of a classifier
(e.g., support vector machine). There are four main benefits of our approach:
(1) Our approach accommodates non-linear additive kernels such as the popular
and intersection kernel; (2) our approach is able to handle both
regions in images and spatio-temporal regions in videos in a unified way; (3)
the feature selection problem is convex, and both problems can be solved using
a scalable reduced gradient method; (4) we point out strong connections with
multiple kernel learning and multiple instance learning approaches.
Experimental results in the PASCAL VOC 2007, MSR Action Dataset II and YouTube
illustrate the benefits of our approach
Training Support Vector Machines Using Frank-Wolfe Optimization Methods
Training a Support Vector Machine (SVM) requires the solution of a quadratic
programming problem (QP) whose computational complexity becomes prohibitively
expensive for large scale datasets. Traditional optimization methods cannot be
directly applied in these cases, mainly due to memory restrictions.
By adopting a slightly different objective function and under mild conditions
on the kernel used within the model, efficient algorithms to train SVMs have
been devised under the name of Core Vector Machines (CVMs). This framework
exploits the equivalence of the resulting learning problem with the task of
building a Minimal Enclosing Ball (MEB) problem in a feature space, where data
is implicitly embedded by a kernel function.
In this paper, we improve on the CVM approach by proposing two novel methods
to build SVMs based on the Frank-Wolfe algorithm, recently revisited as a fast
method to approximate the solution of a MEB problem. In contrast to CVMs, our
algorithms do not require to compute the solutions of a sequence of
increasingly complex QPs and are defined by using only analytic optimization
steps. Experiments on a large collection of datasets show that our methods
scale better than CVMs in most cases, sometimes at the price of a slightly
lower accuracy. As CVMs, the proposed methods can be easily extended to machine
learning problems other than binary classification. However, effective
classifiers are also obtained using kernels which do not satisfy the condition
required by CVMs and can thus be used for a wider set of problems
Structured learning of metric ensembles with application to person re-identification
Matching individuals across non-overlapping camera networks, known as person
re-identification, is a fundamentally challenging problem due to the large
visual appearance changes caused by variations of viewpoints, lighting, and
occlusion. Approaches in literature can be categoried into two streams: The
first stream is to develop reliable features against realistic conditions by
combining several visual features in a pre-defined way; the second stream is to
learn a metric from training data to ensure strong inter-class differences and
intra-class similarities. However, seeking an optimal combination of visual
features which is generic yet adaptive to different benchmarks is a unsoved
problem, and metric learning models easily get over-fitted due to the scarcity
of training data in person re-identification. In this paper, we propose two
effective structured learning based approaches which explore the adaptive
effects of visual features in recognizing persons in different benchmark data
sets. Our framework is built on the basis of multiple low-level visual features
with an optimal ensemble of their metrics. We formulate two optimization
algorithms, CMCtriplet and CMCstruct, which directly optimize evaluation
measures commonly used in person re-identification, also known as the
Cumulative Matching Characteristic (CMC) curve.Comment: 16 pages. Extended version of "Learning to Rank in Person
Re-Identification With Metric Ensembles", at
http://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Paisitkriangkrai_Learning_to_Rank_2015_CVPR_paper.html.
arXiv admin note: text overlap with arXiv:1503.0154
Learning Discriminative Bayesian Networks from High-dimensional Continuous Neuroimaging Data
Due to its causal semantics, Bayesian networks (BN) have been widely employed
to discover the underlying data relationship in exploratory studies, such as
brain research. Despite its success in modeling the probability distribution of
variables, BN is naturally a generative model, which is not necessarily
discriminative. This may cause the ignorance of subtle but critical network
changes that are of investigation values across populations. In this paper, we
propose to improve the discriminative power of BN models for continuous
variables from two different perspectives. This brings two general
discriminative learning frameworks for Gaussian Bayesian networks (GBN). In the
first framework, we employ Fisher kernel to bridge the generative models of GBN
and the discriminative classifiers of SVMs, and convert the GBN parameter
learning to Fisher kernel learning via minimizing a generalization error bound
of SVMs. In the second framework, we employ the max-margin criterion and build
it directly upon GBN models to explicitly optimize the classification
performance of the GBNs. The advantages and disadvantages of the two frameworks
are discussed and experimentally compared. Both of them demonstrate strong
power in learning discriminative parameters of GBNs for neuroimaging based
brain network analysis, as well as maintaining reasonable representation
capacity. The contributions of this paper also include a new Directed Acyclic
Graph (DAG) constraint with theoretical guarantee to ensure the graph validity
of GBN.Comment: 16 pages and 5 figures for the article (excluding appendix
Parsimonious Mahalanobis Kernel for the Classification of High Dimensional Data
The classification of high dimensional data with kernel methods is considered
in this article. Exploit- ing the emptiness property of high dimensional
spaces, a kernel based on the Mahalanobis distance is proposed. The computation
of the Mahalanobis distance requires the inversion of a covariance matrix. In
high dimensional spaces, the estimated covariance matrix is ill-conditioned and
its inversion is unstable or impossible. Using a parsimonious statistical
model, namely the High Dimensional Discriminant Analysis model, the specific
signal and noise subspaces are estimated for each considered class making the
inverse of the class specific covariance matrix explicit and stable, leading to
the definition of a parsimonious Mahalanobis kernel. A SVM based framework is
used for selecting the hyperparameters of the parsimonious Mahalanobis kernel
by optimizing the so-called radius-margin bound. Experimental results on three
high dimensional data sets show that the proposed kernel is suitable for
classifying high dimensional data, providing better classification accuracies
than the conventional Gaussian kernel
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