Fashion pose machine for fashion landmark detection

Ying Hu; Liqiang Xiao; Yongkun Wang; Yaohui Jin

doi:10.1117/12.2515278

29 October 2018 Fashion pose machine for fashion landmark detection

Ying Hu, Liqiang Xiao, Yongkun Wang, Yaohui Jin

Proceedings Volume 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence; 108360Y (2018) https://doi.org/10.1117/12.2515278
Event: 2018 International Conference on Image, Video Processing and Artificial Intelligence, 2018, Shanghai, China

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.

Citation Download Citation

Ying Hu, Liqiang Xiao, Yongkun Wang, and Yaohui Jin "Fashion pose machine for fashion landmark detection", Proc. SPIE 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence, 108360Y (29 October 2018); https://doi.org/10.1117/12.2515278

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