Efficient entropy-based features selection for image retrieval

Authors:
Tsun-Wei Chang;Yo-Ping Huang;Frode Eika Sandnes
Affiliations:
Department of Computer Science and Information Engineering, De Lin Institute of Technology, Tucheng, Taipei County, Taiwan;Department of Electrical Engineering, National Taipei University of Technology, Taipei, Taiwan;Faculty of Engineering, Oslo University College, Oslo, Norway
Venue:
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Year:
2009

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Abstract

Information retrieval systems should provide users quick access to desired information. There are no established ways for inexperienced users to explicitly express queries for retrieving images from ecological databases. This study proposes an entropy-based feature selection strategy for finding images of interest from databases. Six visual features are used to represent birds, and hence used to formulate search queries. The proposed method is tested on a real world bird database and the experimental results demonstrate the effectiveness of the presented work.