Pairing Contour Fragments for Object Recognition

Abstract

Contour fragments are adept to interpret the characteristics of object boundaries, but difficult to encode the information of object interior region. In this paper, inspired by the Gestalt principles that people can perceive object interior region by grouping similar and proximate fragments, we propose to pair contour fragments to encode more information of object interior region. To this end, we propose a pairing algorithm to generate Contour Fragment Pairs (CFPs). According to the proposed algorithm, the fragments of a valid CFP are required to be: co-occurrent over the training images, similar in shape, and proximate with each other. With a valid CFP, we can represent object shape using its fragments and object interior region using the region between its fragments. Finally, we design a boosting algorithm to select and assemble many CFPs into a classifier. The proposed classifier is competent for localizing objects with bounding boxes, delineating boundary and segmenting foreground. Moreover, the method possesses another merit that it only requires annotated bounding boxes as training data. Experiments on the public datasets show that the proposed approach achieves very promising performance.