Image Recognition with Occlusions

We study the problem of how to detect interesting objects appeared in a given image I Our approach is to treat it as a function approximation problem based on an over redundant basis Since the ba sis a library of image templates is over redundant there are in nitely many ways to decompose I To select the best decomposition we rst propose a global optimization procedure that considers a concave cost function derived from a weighted L norm with p This concave cost function selects as few coe cients as possible producing a sparse representation of the image and handle occlusions However it contains multiple local minima We identify all local minima so that a global optimization is possible by visiting all of them Secondly because the number of local minima grows exponentially with the number of templates we investigate a greedy L Matching Pursuit strategy