Image retrieval with relevance feedback based on genetic programming

Authors:
Cristiano D. Ferreira;Ricardo da S. Torres;Marcos André Gonçalves;Weiguo Fan
Affiliations:
University of Campinas -- UNICAMP, Campinas, SP, Brazil;University of Campinas -- UNICAMP, Campinas, SP, Brazil;Federal University of Minas Gerais, Belo Horizonte, MG -- Brazil;Virginia Polytechnic Institute and State University, Blacksburg, VA
Venue:
SBBD '08 Proceedings of the 23rd Brazilian symposium on Databases
Year:
2008

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Abstract

This paper presents a new content-based image retrieval framework with relevance feedback. This framework employs Genetic Programming to discover a combination of descriptors that better characterizes the user perception of image similarity. Several experiments were conducted to validate the proposed framework. These experiments employed three different image databases and color, shape, and texture descriptors to represent the content of database images. The proposed framework was compared with three other relevance feedback methods regarding their efficiency and effectiveness in image retrieval tasks. Experiment results demonstrate the superiority of the proposed method.