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Proceedings Paper

Fashion pose machine for fashion landmark detection
Author(s): Ying Hu; Liqiang Xiao; Yongkun Wang; Yaohui Jin
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Paper Abstract

Combined with deep learning technologies, fashion landmark detection is an efficient method for visual fashion analysis. Existing works mainly focus on eliminating the effect of scale and background, and require prior knowledge of body structure. In this paper, we propose a fashion pose machine which is based on the location method of the landmark for human posture estimation. To increase the accuracy of fashion detection, we utilize convolutional neural network to learn the spatial structure among fashion landmarks in sequential prediction framework, which can eliminate the effect of the clothing placement and model posture on fashion landmark in the image. Our method does not require any prior knowledge of human body structure to learn the dependencies between different landmarks. We evaluated our model on the dataset of FashionAI, and the result showed that our model is 25% better than the state-of-the-art alternative.

Paper Details

Date Published: 29 October 2018
PDF: 5 pages
Proc. SPIE 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence, 108360Y (29 October 2018); doi: 10.1117/12.2515278
Show Author Affiliations
Ying Hu, Shanghai Jiao Tong Univ. (China)
Liqiang Xiao, Shanghai Jiao Tong Univ. (China)
Yongkun Wang, Shanghai Jiao Tong Univ. (China)
Yaohui Jin, Shanghai Jiao Tong Univ. (China)


Published in SPIE Proceedings Vol. 10836:
2018 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

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