Relevance feedback based on genetic programming for image retrieval

Authors:
C. D. Ferreira;J. A. Santos;R. da S. Torres;M. A. Gonçalves;R. C. Rezende;Weiguo Fan
Affiliations:
Institute of Computing, University of Campinas, Campinas, SP 13083-970, Brazil;Institute of Computing, University of Campinas, Campinas, SP 13083-970, Brazil;Institute of Computing, University of Campinas, Campinas, SP 13083-970, Brazil;Department of Computer Science, Federal University of Minas Gerais, Belo Horizonte, MG 31270-010, Brazil;Institute of Computing, University of Campinas, Campinas, SP 13083-970, Brazil;Virginia Polytechnic Institute and State University Blacksburg, VA 24061-0101, USA
Venue:
Pattern Recognition Letters
Year:
2011

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Abstract

This paper presents two content-based image retrieval frameworks with relevance feedback based on genetic programming. The first framework exploits only the user indication of relevant images. The second one considers not only the relevant but also the images indicated as non-relevant. Several experiments were conducted to validate the proposed frameworks. These experiments employed three different image databases and color, shape, and texture descriptors to represent the content of database images. The proposed frameworks were compared, and outperformed six other relevance feedback methods regarding their effectiveness and efficiency in image retrieval tasks.