Sparse Representations for Image Decompositions
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EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
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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problem based on an over-redundant basis, and also account for occlusions, where the basis superposition principle is no longer valid. Since the basis (a library of image templates) is over-redundant, there are infinitely many ways to decompose I. We are motivated to select a sparse/compact representation of I, and to account for occlusions and noise. We then study a greedy and iterative "weighted Lp Matching Pursuit" strategy, with 0