Individualized matching based on logo density for scalable logo recognition

Although many systems based on global or local descriptors have shown promising results for logo recognition, they have handled all logos with the same structure and not considered their diversities. Therefore, with the logo scale increasing, the general way cannot recognize each logo perfectly. To overcome this limitation, we propose a novel strategy to match query and each logo individually using these features. First, a new conception named logo density is introduced as important semantic information for logos. Second, matching density is given according to the logo density and by utilizing it in logistic function an individualized matching strategy is developed to obtain accurate similarity for query and a logo. Finally, we present a fast recognition algorithm based upon bag-of-words model to realize scalable logo recognition. Our method is evaluated on two challenging datasets (our 10,000-class logo dataset and FlickrLogos-27). Experiments demonstrate its superior performance comparing to previous methods.