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

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