Combining color and shape information for appearance-based object recognition using ultrametric spin glass-markov random fields

Abstract

Shape and color information are important cues for object recognition. An ideal system should give the option to use both forms of information, as well as the option to use just one of the two. We present in this paper a kernel method that achieves this goal. It is based on results ofst atistical physics ofd isordered systems combined with Gibbs distributions via kernel functions. Experimental results on a database of 100 objects confirm the effectiveness ofthe proposed approach.