Learning local features for object categorization

Yi Ouyang, Ming Tang, Shi Chen, Jinqiao Wang, Hanqing Lu, Songde Ma

Abstract: In this paper, for every local feature, we propose to learn its similar local features across all positive images, instead of using heuristic distance as similarity measure. Specifically, multiple instance learning (MIL) is employed to simultaneously determine the similar points of a local feature and learn its corresponding discriminative function which can be regarded as some kind of similarity measure. For each local feature, a weak learner is constructed based on such similarity measure. Then AdaBoost selects the most discriminative local features and combines them to form a strong classifier. Experimental results show encouraging performance of our method.