An Approach For Clothing Recommendation Based On Multiple Image Attributes

Currently, many online shopping websites recommend commodities to users according to their purchase history and the behaviors of others who have similar history with the target users. Most recommendations are conducted by commodity tags based similarity search. However, clothing purchase has some specialized characteristics, i.e. users usually don't like to go with the crowd blindly and will not buy the same clothing twice. Moreover, the text tags cannot express clothing features accurately enough. In this paper we propose a novel approach that extracts multi-features from images to analyze its content in different attributes for clothing recommendation. Specifically, a color matrix model is proposed to distinguish split joint clothing. ULBP feature is extracted to represent fabric pattern attribute. PHOG, Fourier, and GIST features are extracted to describe collar and sleeve attributes. Then, some classifiers are trained to classify clothing fabric patterns and split joint types. Experiments based on every attribute and their combinations have been done respectively, and have achieved satisfied results.