关键词: 目标分割, 协同分割, 目标性, 条件随机场

Abstract: An unsupervised segmentation method is proposed in this paper, which collaboratively segments multiple images depicting similar object into foreground and background. Given a set of images, a co-objectness map is computed as the hint about possible foreground locations according to both inter-image and intra-image cues. The map is used as a geometric prior, which describes the location and shape information of object. Then, by bi-segmenting the map, it gets a rough estimation of foreground/background labeling, based on which, a binary classifier of superpixel is trained. The classifier can propagate the appearance distribution（i.e. appearance prior） across the images. Finally, the geometric and appearance priors are incorporated into a conditional random field to achieve object segmentation. The method is tested on the MSRC dataset and recently introduced iCoseg dataset, and shows better results than the state-of-the-art.

Key words: object segmentation, co-segmentation, objectness, conditional random field