Generating vocabulary for global feature representation towards commerce image retrieval

This paper studies the problem of retrieving images by color, texture and shape in the context of visual assisted product recommendation in E-commerce sites. Different from general CBIR applications, commerce image retrieval puts more emphasis on outlier-free ranking (top N) to gain perfect user experience. We suggest to extend the bag-of-words (BoW) model to global feature characterization rather than commonly used histogram based low-level feature representation. Although BoW is a common practice in object recognition, we argue generating feature vocabulary is useful to address the global feature characterization that could be elegantly adapted to domain specific commerce image search. The representation is compact and discriminative, which may adapt with individual websites. Quantitative as well as subjective evaluation demonstrates the functionality of the proposed method. In practice, the vocabulary based global features greatly reduce outliers in top rank images, so that desirable user experience can be obtained in E-Commerce applications.