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A Part-Based and Feature Fusion Method for Clothing Classification

  • Pan Huo
  • Yunhong Wang
  • Qingjie LiuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9916)

Abstract

Clothing recognition and parsing have attracted substantial attention in computer vision community, which contribute to applications like scene recognition, event recognition, e-commerce, etc. In our work, a part-based and feature fusion method is proposed to classify clothing in natural scenes. Firstly, clothing is described with a part-based model, in which a Deformable Part based Model (DPM) and a key point regression method are used to locate the head-shoulder and human torso. Then, a novel Distinctive Efficient Robust Feature (DERF) and four other low-level features are extracted to represent human clothing. Finally, a feature fusion strategy is utilized to promote the classification performance. Experiments are conducted on a new and well labeled image dataset. The experimental results show the efficiency of our proposed method.

Keywords

Image analysis Clothing classification Part-based model Feature fusion 

Notes

Acknowledgments

The work is supported by the Hong Kong, Macao and Taiwan Science and Technology Cooperation Program of China (No. L2015TGA9004).

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.State Key Laboratory of Virtual Reality Technology and SystemBeihang UniversityBeijingChina

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