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2011
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Learning Hierarchical Poselets for Human Parsing

8 years 7 months ago
Learning Hierarchical Poselets for Human Parsing
We consider the problem of human parsing with partbased models. Most previous work in part-based models only considers rigid parts (e.g. torso, head, half limbs) guided by human anatomy. We argue that this representation of parts is not necessarily appropriate for human parsing. In this paper, we introduce hierarchical poselets, a new representation for human parsing. Hierarchical poselets can be rigid parts, but they can also be parts that cover large portions of human bodies (e.g. torso + left arm). In the extreme case, they can be the whole bodies. We develop a structured model to organize poselets in a hierarchical way and learn the model parameters in a max-margin framework. We demonstrate the superior performance of our proposed approach on two datasets with aggressive pose variations.
Yang Wang, Duan Tran, Zicheng Liao
Added 02 Mar 2011
Updated 29 Apr 2011
Type Journal
Year 2011
Where CVPR
Authors Yang Wang, Duan Tran, Zicheng Liao
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