Saliency-seeded region merging: Automatic object segmentation
Junxia Li, Runing Ma, Jundi Ding · 2011
Interactive object segmentation is an active research area in recent decades. The common practice is to leave interactions to be set manually by users in advance. Often times, to get good interactions, one has to struggle with laborious local editing for re-correcting. Given the larger and larger databases occurred nowadays, it is impractical for one to draw manual interactions for each image. In this paper, we are to build a saliency-seeded mechanism to automatically capture good prior interactions. Our motivation is simple: the pixels that have different cues but from the same object are often good candidates for prior interactions, and those pixels at the same time are always with higher salience attracting human attentions. Adopting a newly-proposed idea, i.e., maximal similarity based region merging, we further develop a framework of saliency-seeded region merging for `automatic' interactive segmentation. Extensive experiments and comparisons are conducted on a wide variety of natural images. Results show that our framework can reliably segment many objects out from their surrounding backgrounds.