Randomized sub-vectors hashing for high-dimensional image feature matching

Heng Yang, Qing Wang, Zhoucan He

科研成果: 书/报告/会议事项章节会议稿件同行评审

5 引用 (Scopus)

摘要

High-dimensional image feature matching is an important part of many image matching based problems in computer vision which are solved by local invariant features. In this paper, we propose a new indexing/searching method based on Randomized Sub-Vectors Hashing (called RSVH) for high-dimensional image feature matching. The essential of the proposed idea is that the feature vectors are considered similar (measured by Euclidean distance) when the L2 norms of their corresponding randomized sub-vectors are approximately same respectively. Experimental results have demonstrated that our algorithm can perform much better than the famous BBF (Best-Bin-First) and LSH (Locality Sensitive Hashing) algorithms in extensive image matching and image retrieval applications.

源语言英语
主期刊名MM'08 - Proceedings of the 2008 ACM International Conference on Multimedia, with co-located Symposium and Workshops
705-708
页数4
DOI
出版状态已出版 - 2008
活动16th ACM International Conference on Multimedia, MM '08 - Vancouver, BC, 加拿大
期限: 26 10月 200831 10月 2008

出版系列

姓名MM'08 - Proceedings of the 2008 ACM International Conference on Multimedia, with co-located Symposium and Workshops

会议

会议16th ACM International Conference on Multimedia, MM '08
国家/地区加拿大
Vancouver, BC
时期26/10/0831/10/08

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