Feature space optimization for content-based image retrieval

Abstract

Substantial benefits can be gained from effective Relevance Feedback techniques in content-based image retrieval. However, existing techniques are limited due to computational cost and/or by being restricted to linear transformations on the data. In this study we analyze the role of nonlinear transformations in relevance feedback. 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 features space by means of linear and nonlinear transformation functions. Experimental results on real data sets 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%. Our results indicate that nonlinear transformations have a great potential in capturing the user's interests in image retrieval and should be further analyzed employing other learning/optimization mechanisms.