A number of algorithms exist in measuring clothing similarity for clothing recommendations in E-commerce. The clothing similarity mostly depends on its shape, texture and style. In this paper we introduce three models of defining feature space for clothing recommendations. The sketch-based image search mainly focuses on defining similarity of clothing in contour dimension. The spatial bagof-feature approach is employed to measure the clothing similarity of local image patterns. Finally, we introduce a query adaptive shape model which combines shape characteristics and labels of clothing, in order to take the semantic information of clothing. A large number of simulations are given to show the feasibility and performance of the clothing recommendations by using content-based image search.
CITATION STYLE
Wang, H., Zhou, Z., Xiao, C., & Zhang, L. (2015). Content based image search for clothing recommendations in E-Commerce. In Multimedia Data Mining and Analytics: Disruptive Innovation (pp. 253–267). Springer International Publishing. https://doi.org/10.1007/978-3-319-14998-1_11
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