25,982 research outputs found
Quality-based Multimodal Classification Using Tree-Structured Sparsity
Recent studies have demonstrated advantages of information fusion based on
sparsity models for multimodal classification. Among several sparsity models,
tree-structured sparsity provides a flexible framework for extraction of
cross-correlated information from different sources and for enforcing group
sparsity at multiple granularities. However, the existing algorithm only solves
an approximated version of the cost functional and the resulting solution is
not necessarily sparse at group levels. This paper reformulates the
tree-structured sparse model for multimodal classification task. An accelerated
proximal algorithm is proposed to solve the optimization problem, which is an
efficient tool for feature-level fusion among either homogeneous or
heterogeneous sources of information. In addition, a (fuzzy-set-theoretic)
possibilistic scheme is proposed to weight the available modalities, based on
their respective reliability, in a joint optimization problem for finding the
sparsity codes. This approach provides a general framework for quality-based
fusion that offers added robustness to several sparsity-based multimodal
classification algorithms. To demonstrate their efficacy, the proposed methods
are evaluated on three different applications - multiview face recognition,
multimodal face recognition, and target classification.Comment: To Appear in 2014 IEEE Conference on Computer Vision and Pattern
Recognition (CVPR 2014
Multimodal Multipart Learning for Action Recognition in Depth Videos
The articulated and complex nature of human actions makes the task of action
recognition difficult. One approach to handle this complexity is dividing it to
the kinetics of body parts and analyzing the actions based on these partial
descriptors. We propose a joint sparse regression based learning method which
utilizes the structured sparsity to model each action as a combination of
multimodal features from a sparse set of body parts. To represent dynamics and
appearance of parts, we employ a heterogeneous set of depth and skeleton based
features. The proper structure of multimodal multipart features are formulated
into the learning framework via the proposed hierarchical mixed norm, to
regularize the structured features of each part and to apply sparsity between
them, in favor of a group feature selection. Our experimental results expose
the effectiveness of the proposed learning method in which it outperforms other
methods in all three tested datasets while saturating one of them by achieving
perfect accuracy
Learning Multiple Visual Tasks while Discovering their Structure
Multi-task learning is a natural approach for computer vision applications
that require the simultaneous solution of several distinct but related
problems, e.g. object detection, classification, tracking of multiple agents,
or denoising, to name a few. The key idea is that exploring task relatedness
(structure) can lead to improved performances.
In this paper, we propose and study a novel sparse, non-parametric approach
exploiting the theory of Reproducing Kernel Hilbert Spaces for vector-valued
functions. We develop a suitable regularization framework which can be
formulated as a convex optimization problem, and is provably solvable using an
alternating minimization approach. Empirical tests show that the proposed
method compares favorably to state of the art techniques and further allows to
recover interpretable structures, a problem of interest in its own right.Comment: 19 pages, 3 figures, 3 table
Person Re-Identification by Deep Joint Learning of Multi-Loss Classification
Existing person re-identification (re-id) methods rely mostly on either
localised or global feature representation alone. This ignores their joint
benefit and mutual complementary effects. In this work, we show the advantages
of jointly learning local and global features in a Convolutional Neural Network
(CNN) by aiming to discover correlated local and global features in different
context. Specifically, we formulate a method for joint learning of local and
global feature selection losses designed to optimise person re-id when using
only generic matching metrics such as the L2 distance. We design a novel CNN
architecture for Jointly Learning Multi-Loss (JLML) of local and global
discriminative feature optimisation subject concurrently to the same re-id
labelled information. Extensive comparative evaluations demonstrate the
advantages of this new JLML model for person re-id over a wide range of
state-of-the-art re-id methods on five benchmarks (VIPeR, GRID, CUHK01, CUHK03,
Market-1501).Comment: Accepted by IJCAI 201
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