Fusion of covariance matrices of PCA and FLD

Guru, D. S. and Suraj, M. G. and Manjunath, S. (2011) Fusion of covariance matrices of PCA and FLD. PATTERN RECOGNITION LETTERS, 32 (3). pp. 432-440. ISSN 0167-8655

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Abstract

In this paper, we propose a novel approach for fusing two classifiers, specifically classifiers based on subspace analysis, during feature extraction. A method of combining the covariance matrices of the Principal Component Analysis (PCA) and Fisher Linear Discriminant (FLD) is presented. Unlike other existing fusion strategies which fuse classifiers either at data level, or at feature level or at decision level, the proposed work combines two classifiers while extracting features introducing a new unexplored area for further research. The covariance matrices of PCA and FLD are combined using a product rule to preserve the natures of both covariance matrices with an expectation to have an increased performance. In order to show the effectiveness of the proposed fusion method, we have conducted a visual simulation on iris data. The proposed model has also been tested by performing clustering on standard datasets such as Zoo, Wine, and Iris. To study the versatility of the proposed method we have carried out an experimentation on sports video shot retrieval problem. The experimental results signify that the proposed fusing approach has an improved performance over individual classifiers. (C) 2010 Elsevier B.V. All rights reserved.

Item Type:	Article
Uncontrolled Keywords:	Classifier fusion; Appearance based approach; Covariance matrix; Data clustering; Video retrieval
Subjects:	D Physical Science > Computer Science
Divisions:	Department of > Computer Science
Depositing User:	Users 23 not found.
Date Deposited:	21 Jun 2019 09:39
Last Modified:	21 Jun 2019 09:39
URI:	http://eprints.uni-mysore.ac.in/id/eprint/2581

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