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Web image retrieval is a research area that is receiving a lot of attention in the last few years due to the growing availability of images on the Web. Since content-based image retrieval is still considered very difficult and expensive in the Web context, most current large-scale Web image search engines use textual descriptions to represent the content of the Web images. In this paper we present a study about the usage of genetic programming (GP) to address the problem of image retrieval on the World Wide Web by using textual sources of evidence and textual queries. We investigate several parameter of choices related to the usage of a framework previously proposed by us. The proposed framework uses GP to provide a good solution to combine multiple textual sources of evidence associated with the Web images. Experiments performed using a collection with more than 195,000 images extracted from the Web showed that our evolutionary approach outperforms the best baseline we used with gains of 22.36 % in terms of mean average precision.

Evaluation of parameters for combining multiple textual sources of evidence for Web image retrieval using genetic programming