An Approach Of Subjective Clothing Styles Recognition Based On Distance Metric Learning And Multi-view Learning

With the development of electronic commerce, more and more people are interested in buying clothes online. In the field of computer vision, how to recognize the styles of clothes becomes a piece of meaningful work. The accurate identification of clothing styles can lay a good foundation for the following fields:automatic labelling, retrieval based on the content of the clothing styles and individual recommendation, and even collocating. However, existing methods mostly focus on objective styles, rather than subjective ones. And the problem of identifying multiple subjective styles has rarely been addressed before. Therefore, this paper puts forward a method to identify multiple clothing subjective styles at the same time.In this paper, we crawl many clothing images with manual labeled multiple subjective styles and their whole label information from the Internet; Then do pose estimation and extract features on these images; After that, in order to get more suitable features, we propose a multi-label distance metric learning model inspired by single-label distance metric learning. Next, in order to increase the effectiveness of clothing subjective styles recognition using the text information, we put forwards a multi-label multi-view learning model, and this model is applied to features obtained by last step. Finally, we adopt multi-label classification method on the features learned from the above models, and improve the performances in subject clothes styles recognition.The experimental results show that the proposed method can effectively recognize the subjective styles of the clothing images.