Weakly Supervised Part Proposal Segmentation From Multiple Images

Weakly supervised local part segmentation is challenging, due to the difficulty of modeling multiple local parts from image level prior. In this paper, we propose a new weakly supervised local part proposal segmentation method based on the observation that local parts will keep fixed along the object pose variations. Hence, the local part can be segmented by capturing object pose variations. Based on such observation, a new local part proposal segmentation model is proposed. Three aspects, such as shape similarity-based cosegmentation, shape matching-based part detection and segmentation, and graph matching-based part assignment are considered. A part segmentation energy function is first proposed. Four terms, such as MRF-based single image segmentation term, shape feature-based foreground consistency term, NCuts-based part segmentation term, and two-order graphs matching based part consistency term, are contained. Then, a three sub-minimization-based energy minimization method is proposed to accomplish approximation solution. Finally, we verify our method based on three image data sets (PASCAL VOC 2008 Part data set, UCB Bird data set, and Cat-Dog data set), and one video data set (UCF Sports) data set. The experimental results demonstrate a better segmentation performance compared with the existing object cosegmentation and part proposal generation methods.