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Image matching and retrieval in the domain of clothing, as used in online shopping for recommending similar products, is often distracted by the existence of a mannequin/model wearing the product. The existence of a model adds clutter to both the shape and color features of the product. In this paper, we propose a novel image pre-processing pipeline that minimizes skin and background segments generated from generic GraphCut segmentation. Experiments judged by human subjects show very promising gains of around 23% in retrieval precision of the top 25 similar products compared to the baseline system.

Using Skin Segmentation to Improve Similar Product Recommendations in Online Clothing Stores