Improving local descriptors by embedding global and local spatial information

Authors:
Tatsuya Harada;Hideki Nakayama;Yasuo Kuniyoshi
Affiliations:
Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan;Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan;Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan
Venue:
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Year:
2010

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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.