Logo recognition and localization in real-world images by using visual patterns

By describing spatial relationships between feature points, we present promising logo recognition and localization, which are verified based on two state-of-the-art datasets. Given features points on the query logo, similar features on test images are efficiently found by locality sensitive hashing. After filtering out outliers, candidate regions are found by the mean-sift algorithm, and each region is compared with the logo by jointly considering visual word histogram and visual patterns. Evaluation results show that visual patterns more appropriately describe logos and provide better performance than previous approaches.