Product image search with regional evidences

In the task of product image search, the database consists of clean versions of product images, while the query photos are often captured from mobile phone cameras under uncontrolled conditions. Conventional methods usually adopt the SIFT based bag-of-words (BoW) representation of the whole query image, which suffers from the interference of background noise. To address the problem, we extract multiple candidate regions from the query image and compute the regional similarity to database images individually. Then a verification strategy is proposed to evaluate the similarity based on regional semantic evidences. With the proposed method, we can not only improve the search accuracy, but also obtain the location of the product in the query image. Extensive experiments on two public datasets demonstrate the effectiveness of our method.