Point pattern matching with locality preserving descriptors

Authors:
Weidong Yan;Zheng Tian;Chengcai Leng;Lulu Pan
Affiliations:
School of Science, Northwestern Polytechnical University, Xi'an;School of Science, Northwestern Polytechnical University, Xi'an and State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing;School of Science, Northwestern Polytechnical University, Xi'an;School of Science, Northwestern Polytechnical University, Xi'an
Venue:
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
Year:
2009

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Abstract

This paper proposes a novel feature descriptor - locality preserving descriptor to the problem of point pattern matching. The idea behind a locality preserving is to map points that are nearby in the data space into points that are nearby in the feature space. The feature descriptor optimally preserves the neighborhood structure of the data set, and is invariant to translation, scale, and rotation. We use of the locality preserving descriptor and combine the continuity constraint for point pattern matching.