Logo Recognition via Improved Topological Constraint

Real-world logo recognition is challenging mainly due to various viewpoints and different lighting conditions. Currently, the most popular approaches are usually based on bag-of-words model due to their good performance. However, their shortcomings lie in two main aspects: (1) wrong recognition results caused by mismatching of keypoints. (2) high computational complexity and extra noise caused by a large number of keypoints which are irrelevant to the target logo. To address these two problems, we propose a new approach which combines feature selection and topological constraint for logo recognition. Firstly, feature selection is applied to filter out most of the irrelevant keypoints. Secondly, an improved topological constraint, which considers the relative position between a keypoint and its neighboring points, is proposed to reduce the number of mismatched keypoints. It is proven in this paper that the proposed constraint can remove the keypoints which are not on the same planar surface with the others from the k nearest neighbors of a keypoint. This property is very important to logo recognition because logos are planar objects in real world. The proposed approach is evaluated on two challenging logo recognition benchmarks, FlickrLogos-32 and FlickrLogos-27, and the experimental results show its effectiveness compared to other popular methods.