SIGGRAPH Asia' 2012

Discontinuity-Aware Video Object Cutout

Fan Zhong,Xueying Qin,Qunsheng Peng,Xiangxu Meng
Shandong University, Zhejiang University

Existing video object cutout systems can only deal with limited cases. They usually require detailed user interactions to segment real-life videos, which often suffer from both inseparable statistics (similar appearance between foreground and background) and temporal discontinuities (e.g. large movements, newly-exposed regions following disocclusion or topology change). In this paper, we present an efficient video cutout system to meet this challenge. A novel directional classifier is proposed to handle temporal discontinuities robustly, and then multiple classifiers are incorporated to cover a variety of cases. The outputs of these classifiers are integrated via another classifier, which is learnt from real examples. The foreground matte is solved by a coherent matting procedure, and remaining errors can be removed easily by additive spatio-temporal local editing. Experiments demonstrate that our system performs more robustly and more intelligently than existing systems in dealing with various input types, thus saving a lot of user labor and time.


Including most training and testing examples used in the paper.


@article {zhong2012,

author = {Zhong, Fan and Qin, Xueying and Peng, Qunsheng and Meng, Xiangxu},

title = {Discontinuity-Aware Video Object Cutout},

journal = {ACM Trans. Graph. (SIGGRAPH Asia 2012},

year = {2012},

volume = {31},

number = {6},

issn = {0730-0301},

pages = {175:1--175:10},

articleno = {175},

numpages = {10}


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