Exploiting distinctive topological constraint of local feature matching for logo image recognition

Robust local feature matching plays an important role in the challenging task of logo image recognition. Most traditional methods consider the individual local feature but ignore the affine-invariant geometric relationship among the adjacent local features, which is essential to reduce the number of mismatching. In addition, they do matching for all of the local features and ignore that many ones are insignificant, which increase the probability of mismatching and the computation complexity. To address the two limitations, we propose a robust matching method to get the better matching results by exploiting the distinctive topological constraint together with the feature selection. In the proposed method, first we employ the distinctive topological constraint to enhance the describing ability of local features, which makes full use of the affine-invariant geometric relationship among adjacent local features for more accurate local feature matching. Second, we utilize the feature selection algorithm based on the mutual information (MI), to filter out most insignificant local features before matching, which is efficient and effective to guarantee the performance of local feature matching. We evaluated the proposed method on two challenging datasets, i.e, FlickrLogos-32 and FlickrLogos-27, and achieve superior performance against the state-of-the-art methods in the literature.