Sparse representation based visual element analysis

Modern clothes are designed based on various visual elements of different fashion styles. Traditional vision-based clothes recommendation methods focused on searching clothes which are similar with user preferred samples in the aspects of colors and partial shape elements. In this paper, we propose a method of recommending clothes by mining visual elements of different fashion styles. Independent Component Analysis (ICA) is employed to extract sparse features, and then Term-Frequency (TF) analysis is applied to discover visual elements from these independent components. Finally, we test three ranking metrics for clothes recommendation including Euclidian distance of TFs, Cosine distance of TFs and Minimum TF. Experimental results based on web commercial images demonstrate the effectiveness of the proposed method.