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Bypass information-theoretic shape similarity from non-rigid points-based alignment

In this paper we present several information-theoretic similiarity measures for shape retrieval in combination with non-rigid registration processes. The challenging property of these measures is that they are bypass divergences, that is, do not require the estimation of the probability density function for each shape. After presenting the dissimilarities and proposing some new ones, we analyze their performance in terms of average recall for a very difficult database (GatorBait) with many classes, few examples and high degree of intra-class variability. We also test these measures in a subset of the the well known MPEG7 part B database. Our experiments show that the Henze-Penrose divergence outperforms the other ones in 2D shape retrieval. We uncover also very competitive and more efficient measures in both cases.