Seeds-Based Part Segmentation by Seeds Propagation and Region Convexity Decomposition
Object part segmentation is an important and challenging task in computer vision. The existing supervised part segmentation methods need pixel level training data which leads to a huge workload for the user. In this paper a weakly supervised part segmentation method is proposed which segments part regions from multiple images by only several seeds on an image. Two aspects such as seed propagation among multiple images and part generation from seeds are considered. The first aspect is to generate part seeds in each image in terms of seed propagation which is accomplished by part matching combined with latent object regions. We fuse the local part matching and global shape cosegmentation to avoid the noise propagation. The second aspect is to segment part regions from object regions and part seeds which is formulated as the object shape decomposition model. The shape convexity analysis and seed location are fused to accomplish the decomposition and the final part segmentation. The proposed method is verified on the PASCAL 2010 dataset Bird dataset Cat-Dog dataset and UCF Sports Actions dataset. Experimental results demonstrate the effectiveness of the proposed method with larger intersection over union (IOU) values compared with existing weakly supervised part generation methods.