Optimal dimension reduction for image retrieval with correlation metrics

We investigate content-based image retrieval employing a representation of images based on the statistics of their spectral components and a new linear dimension reduction technique. This linear dimension reduction technique is designed to optimize class separation with respect to metrics derived from cross-correlation of spectral histograms. Our approach to retrieval involves a preliminary classification step to index images in a database followed by a class-by-class retrieval step. We carry out several experiments with the Corel database and compare the outcome with several results previously reported in the literature.