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	Adaptive Figure-Ground Classification
	Chen, Yisong ; Chan, Antoni B. ; Wang, Guoping
	2012
关键词	IMAGE SEGMENTATION ENERGY MINIMIZATION EXTRACTION CUTS
英文摘要	We propose an adaptive figure-ground classification algorithm to automatically extract a foreground region using a user-provided bounding-box. The image is first over-segmented with an adaptive mean-shift algorithm, from which background and foreground priors are estimated. The remaining patches are iteratively assigned based on their distances to the priors, with the foreground prior being updated online. A large set of candidate segmentations are obtained by changing the initial foreground prior. The best candidate is determined by a score function that evaluates the segmentation quality. Rather than using a single distance function or score function, we generate multiple hypothesis segmentations from different combinations of distance measures and score functions. The final segmentation is then automatically obtained with a voting or weighted combination scheme from the multiple hypotheses. Experiments indicate that our method performs at or above the current state-of-the-art on several datasets, with particular success on challenging scenes that contain irregular or multiple-connected foregrounds. In addition, this improvement in accuracy is achieved with low computational cost.; Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Engineering, Electrical & Electronic; EI; CPCI-S(ISTP); 5
语种	英语
DOI标识	10.1109/CVPR.2012.6247733
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/406018]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Chen, Yisong,Chan, Antoni B.,Wang, Guoping. Adaptive Figure-Ground Classification. 2012-01-01.

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