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Embedded sparse representation for i...

Huang, Jin.

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Embedded sparse representation for image classification.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Embedded sparse representation for image classification./
Author:	Huang, Jin.
Description:	42 p.
Notes:	Source: Masters Abstracts International, Volume: 49-03, page: 1868.
Contained By:	Masters Abstracts International49-03.
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1488363
ISBN:	9781124446776

Embedded sparse representation for image classification.
Huang, Jin.

Embedded sparse representation for image classification. - 42 p.

Source: Masters Abstracts International, Volume: 49-03, page: 1868.

Thesis (M.S.)--The University of Texas at Arlington, 2010.

Image classification, such as face recognition and scene categorization, is an important research area in computer vision over the last decade. It has been successfully applied to many image analysis applications. Images usually have a large number of features, hence the dimensionality reduction methods are often employed before the subsequent classification to improve the classification results. A lot of methods have been proposed, including but not limited to PCA, ICA, LDA, and Bayesian Framework.

ISBN: 9781124446776Subjects--Topical Terms:

626642
Computer Science.

Embedded sparse representation for image classification.
LDR:02586nam 2200313 4500 001 1401381
005 20111017083934.5
008 130515s2010 ||||||||||||||||| ||eng d
020 $a 9781124446776
035 $a (UMI)AAI1488363
035 $a AAI1488363
040 $a UMI $c UMI
100 1 $a Huang, Jin. $3 1263002
245 1 0 $a Embedded sparse representation for image classification.
300 $a 42 p.
500 $a Source: Masters Abstracts International, Volume: 49-03, page: 1868.
500 $a Adviser: Heng Huang.
502 $a Thesis (M.S.)--The University of Texas at Arlington, 2010.
520 $a Image classification, such as face recognition and scene categorization, is an important research area in computer vision over the last decade. It has been successfully applied to many image analysis applications. Images usually have a large number of features, hence the dimensionality reduction methods are often employed before the subsequent classification to improve the classification results. A lot of methods have been proposed, including but not limited to PCA, ICA, LDA, and Bayesian Framework.
520 $a Recently, compressive sensing and sparse learning have been widely studied and applied into computer vision research. Ma et al. suggested a new method called Sparse Representation Classification (SRC). This new framework provides new insights into two critical issues in image classification: feature extraction and robustness to occlusion. Motivated by this method, we proposed a new method called embedded sparse representation. This masterpiece combined the dimension reduction and classification into one. We proposed three possible objective functions and discussed the possible optimization ways to tackle them. During the optimization, we used Gibbs Optimization method to alternatively find the optimal subspace representation and the sparse weight factor.
520 $a In the experiments, we verified the convergence of our method. Our method successfully extracted the subspace structure and got a better performance than SRC and other classical methods. It has also been shown that our method has the potential to be extended to other general high dimensional data. The possible improvement and future work have also been discussed.
590 $a School code: 2502.
650 4 $a Computer Science. $3 626642
690 $a 0984
710 2 $a The University of Texas at Arlington. $b Computer Science & Engineering. $3 1023758
773 0 $t Masters Abstracts International $g 49-03.
790 1 0 $a Huang, Heng, $e advisor
790 1 0 $a Ding, Chris $e committee member
790 1 0 $a Lei, Jeff $e committee member
790 $a 2502
791 $a M.S.
792 $a 2010
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1488363