Improving Local Descriptors by Embedding Global and Local Spatial Information

Tatsuya Harada, Hideki Nakayama, Yasuo Kuniyoshi

Abstract: In this paper, we present a novel problem: “Given local descriptors, how can we incorporate both local and global spatial information into the descriptors, and obtain compact and discriminative features?” To address this problem, we proposed a general framework to improve any local descriptors by embedding both local and global spatial information. In addition, we proposed a simple and powerful combination method for different types of features. We evaluated the proposed method for the most standard scene and object recognition dataset, and confirm the effectiveness of the proposed method from the viewpoint of speed and accuracy.