Implicit Sparse Shape Representation: A Unified Framework for Object Segmentation and Recognition

Abstract

Given a classified probabilistic shape dictionary, and an image with a shape similar to some of the elements in the dictionary, this letter introduces a sparse representation based framework with a twofold goal. First, to select a sparse shape combination from the dictionary that best represents the shape, and second, to accurately segment the image taking into account both the sparse shape combination and the image information. A new energy function that combines the region-based segmentation with sparse representation is introduced to accomplish both goals simultaneously. The experimental results show the superior segmentation and recognition capabilities of the proposed model.