Supervised Learning of Diffusion Distance to Improve Histogram Matching

n this paper we propose a learning method properly designed for histogram comparison. We based our approach on the so called diffusion distance which has been introduced to improve the robustness against the quantization effect and the limitations of the standard bin-to-bin distance computation. We revised the diffusion distance definition in order to cast the histogram matching as a distance metric learning problem. In particular, we exploit the Large Margin Nearest Neighbor (LMNN) classification procedure to introduce a supervised version of the standard nearest neighbor (NN) classification paradigm. We evaluate our method on several application domains namely, brain classification, texture classification, and image classification. In all the experiments our approach shown promising results in comparison with other similar methods.