Multi-dimensional evolutionary feature synthesis for content-based image retrieval

Low-level features (also called descriptors) play a central role in content-based image retrieval (CBIR) systems. Features are various types of information extracted from the content and represent some of its characteristics or signatures. However, especially the (low-level) features, which can be extracted automatically usually lack the discrimination power needed for accurate description of the image content and may lead to a poor retrieval performance. In order to efficiently address this problem, in this paper we propose a multi- dimensional evolutionary feature synthesis technique, which seeks for the optimal linear and non-linear operators so as to synthesize highly discriminative set of features in an optimal dimension. The optimality therein is sought by the multi-dimensional particle swarm optimization method along with the fractional global-best formation technique. Clustering and CBIR experiments where the proposed feature synthesizer is evolved using only the minority of the image database, demonstrate a significant performance improvement and exhibit a major discrimination between the features of different classes.