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A Latent Clothing Attribute Approach for Human Pose Estimation

  • Weipeng ZhangEmail author
  • Jie Shen
  • Guangcan Liu
  • Yong Yu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9003)

Abstract

As a fundamental technique that concerns several vision tasks such as image parsing, action recognition and clothing retrieval, human pose estimation (HPE) has been extensively investigated in recent years. To achieve accurate and reliable estimation of the human pose, it is well-recognized that the clothing attributes are useful and should be utilized properly. Most previous approaches, however, require to manually annotate the clothing attributes and are therefore very costly. In this paper, we shall propose and explore a latent clothing attribute approach for HPE. Unlike previous approaches, our approach models the clothing attributes as latent variables and thus requires no explicit labeling for the clothing attributes. The inference of the latent variables are accomplished by utilizing the framework of latent structured support vector machines (LSSVM). We employ the strategy of alternating direction to train the LSSVM model: In each iteration, one kind of variables (e.g., human pose or clothing attribute) are fixed and the others are optimized. Our extensive experiments on two real-world benchmarks show the state-of-the-art performance of our proposed approach.

Keywords

Latent Variable Action Recognition Human Part Short Sleeve Human Pose Estimation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Weipeng Zhang
    • 1
    Email author
  • Jie Shen
    • 1
  • Guangcan Liu
    • 2
  • Yong Yu
    • 1
  1. 1.Shanghai Jiao Tong UniversityShanghaiChina
  2. 2.Nanjing University of Information Science and TechnologyNanjingChina

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