Clothing Retrieval Based on Local Similarity with Multiple Images
Masaru Mizuochi, Asako Kanezaki, Tatsuya Harada · 2014
Recently, the online shopping market has been expanded, which has advanced studies of clothing retrieval via image search. For this study, we develop a novel clothing retrieval system considering local similarity, where users can retrieve their desired clothes which are globally similar to an image and partially similar to another image. We propose a method of coding global features by merging local descriptors extracted from multiple images. Furthermore, we design a system that re-evaluates output of similar image search by the similarity of local regions. We demonstrated that our method increased the probability of users finding their desired clothes from 39.7%-55.1%, compared to a standard similar image search system with global features of a single image. Statistical significance is proven using t-tests.