Image retrieval employing genetic dissimilarity weighting and feature space transformation functions
Letricia P. S. Avalhais, Sérgio Francisco da Silva, Jose Fernando Rodrigues Junior, Agma J. M. Traina · 2012
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%.