Improving similarity measures of histograms using smoothing projections

Selection of a proper similarity measure is an essential consideration for a success of many methods. In this study, similarity measures are analyzed in the context of ordered histogram type data, such as gray-level histograms of digital images or color spectra. Furthermore, the performance of the studied similarity measures can be improved using a smoothing projection, called neighbor-bank projection. Especially, with distance functions utilizing statistical properties of data, e.g., the Mahalanobis distance, a significant improvement was achieved in the classification experiments on real data sets, resulting from the use of a priori information related to ordered data. The proposed projection seems also to be applicable for dimensional reduction of histograms and to represent sparse data in a more tight form in the projection subspace.