Entropy-of-likelihood feature selection for image correspondence

Feature points for image correspondence are often selected according to subjective criteria (e.g. edge density, nostrils). In this paper, we present a general, nonsubjective criterion for selecting informative feature points, based on the correspondence model itself. We describe the approach within the framework of the Bayesian Markov random field (MRF) model, where the degree of feature point information is encoded by the entropy of the likelihood term. We propose that feature selection according to minimum entropy-of-likelihood (EOL) is less likely to lead to correspondence ambiguity, thus improving the optimization process in terms of speed and quality of solution. Experimental results demonstrate the criterion´s ability to select optimal features points in a wide variety of image contexts (e.g. objects, faces). Comparison with the automatic Kanade-Lucas-Tomasi feature selection criterion shows correspondence to be significantly faster with feature points selected according to minimum EOL in difficult correspondence problems.