Image retrieval employing genetic dissimilarity weighting and feature space transformation functions

Authors:
Letricia P. S. Avalhais;Sergio F. da Silva;Jose F. Rodrigues, Jr.;Agma J. M. Traina
Affiliations:
University of São Paulo, São Carlos, Brazil;University of São Paulo, São Carlos, Brazil;University of São Paulo, São Carlos, Brazil;University of São Paulo, São Carlos, Brazil
Venue:
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Year:
2012

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Abstract

We present two promising Relevance Feedback methods based on Genetic Algorithms used to enhance the performance on the task of image retrieval according to the user's interests. The first method adjusts the dissimilarity function by using weighting functions while the second method redefines the feature space by means of linear and nonlinear transformation functions. Experimental results on real datasets demonstrate that our methods are effective and the results show that the transformation approach outperforms the weighting approach, achieving a precision gain of up to 70%.