A genetic programming framework for content-based image retrieval

  • Authors:

  • Ricardo da S. Torres;Alexandre X. Falcão;Marcos A. Gonçalves;João P. Papa;Baoping Zhang;Weiguo Fan;Edward A. Fox

  • Affiliations:

  • Institute of Computing, University of Campinas-UNICAMP, 13083-970 Campinas, SP, Brazil;Institute of Computing, University of Campinas-UNICAMP, 13083-970 Campinas, SP, Brazil;Department of Computer Science, Federal University of Minas Gerais, Belo Horizonte, MG, Brazil;Institute of Computing, University of Campinas-UNICAMP, 13083-970 Campinas, SP, Brazil;Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA;Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA;Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA

  • Venue:
  • Pattern Recognition
  • Year:
  • 2009

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Abstract

The effectiveness of content-based image retrieval (CBIR) systems can be improved by combining image features or by weighting image similarities, as computed from multiple feature vectors. However, feature combination do not make sense always and the combined similarity function can be more complex than weight-based functions to better satisfy the users' expectations. We address this problem by presenting a Genetic Programming framework to the design of combined similarity functions. Our method allows nonlinear combination of image similarities and is validated through several experiments, where the images are retrieved based on the shape of their objects. Experimental results demonstrate that the GP framework is suitable for the design of effective combinations functions.