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2010

Unsupervised Object Segmentation with a Hybrid Graph Model (HGM)

8 years 5 months ago
Unsupervised Object Segmentation with a Hybrid Graph Model (HGM)
—In this work, we address the problem of performing class-specific unsupervised object segmentation, i.e., automatic segmentation without annotated training images. Object segmentation can be regarded as a special data clustering problem where both class-specific information and local texture/color similarities have to be considered. To this end, we propose a hybrid graph model (HGM) that can make effective use of both symmetric and asymmetric relationship among samples. The vertices of a hybrid graph represent the samples and are connected by directed edges and/or undirected ones, which represent the asymmetric and/or symmetric relationship between them, respectively. When applied to object segmentation, vertices are superpixels, the asymmetric relationship is the conditional dependence of occurrence, and the symmetric relationship is the color/texture similarity. By combining the Markov chain formed by the directed subgraph and the minimal cut of the undirected subgraph, the object...
Guangcan Liu, Zhouchen Lin, Yong Yu, Xiaoou Tang
Added 29 Jan 2011
Updated 29 Jan 2011
Type Journal
Year 2010
Where PAMI
Authors Guangcan Liu, Zhouchen Lin, Yong Yu, Xiaoou Tang
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