Efficient logo recognition by local feature groups

This paper presents a method for efficient and scalable logo recognition. Using generalized Hough transform to identify local features that are invariant across images, we can efficiently add spatial information into groups of local features and enhance the discriminative power of local feature. Our method is more flexible and efficient compared with state-of-the-art methods that merge features into groups. To fully exploit the information that different logo images provide, we employ a reference-based image representation scheme to represent training and testing images. Experiments on challenging datasets show that our method is efficient and scalable and achieves state-of-the-art performance.