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Simultaneous Feature Selection and Classification for Relevance Feedback in Image Retrieval

Prasanna, Reshma and Ramakrishnan, KR and Bhattacharyya, Chiranjib (2003) Simultaneous Feature Selection and Classification for Relevance Feedback in Image Retrieval. In: Conference on Convergent Technologies for Asia-Pacific Region TENCON 2003, 15-17 October, Bangalore,India, Vol.2, 576-580.

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Abstract

In image retrieval, relevance feedback uses information, obtained interactively from the user, to understand the user's perceptions of a query image and to improve retrieval accuracy. We propose simultaneous relevant feature selection and classification using the samples provided by the user to improve retrieval accuracy. The classifier is defined by a separating hyperplane, while the sparse weight vector characterizing the hyperplane defines a small set of relevant features. This set of relevant features is used for classification and can be used for analysis at a later stage. Mutually exclusive sets of images are shown to the user at each iteration to obtain maximum information from the user. Experimental results show that our algorithm performs better than the feature relevance weighting and feature selection schemes and comparably with the classification scheme using SVMs, in terms of retrieval accuracy, and it has the advantage of being faster than the classification scheme using SVMs.

Item Type: Conference Paper
Additional Information: 1990 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 23 Dec 2005
Last Modified: 19 Sep 2010 04:22
URI: http://eprints.iisc.ernet.in/id/eprint/4755

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