SHAPE-INCLUDED LABEL-CONSISTENT DISCRIMINATIVE DICTIONARY LEARNING: AN APPROACH TO DETECT AND SEGMENT MULTI-CLASS OBJECTS IN IMAGES

This paper introduces a segmentation approach, where a discriminative dictionary with objects' shape information is learned, followed by a sparse representation based segmentation process. In contrast with state-of-the-art sparse representation classification methods using discriminative dictionary learning, the proposed method learns a discriminative dictionary containing both intensity and shape information of object classes, in which shape information is collected and represented in the form of binarized masks. Object segmentation is achieved through an iterative process, including sparse representation, shape estimation, and shape refinement. The introduced method is evaluated and compared to state-of-the-art sparse representation based segmentation methods, and demonstrated better segmentation performance.