Multiple randomized sub-vectors quantization hashing for high-dimensional image feature matching

Heng Yang, Qing Wang, Zhoucan He

科研成果: 期刊稿件文章同行评审

2 引用 (Scopus)

摘要

This paper proposes a new indexing algorithm based on multiple randomized sub-vectors quantization hashing (MRSVQH) for efficient high-dimensional image feature matching. The proposed MRSVQH algorithm quantizes feature vectors according to the L2 norms of the randomized sub-vectors and hashes feature vectors to their corresponding hash buckets. Such index structures are built for multiple times in order to increase the searching accuracy. On the query stage, the searching process is limited only in the feature vectors that have the same hash value to the query one. Experimental results demonstrate that our MRSVQH algorithm can significantly improve the performance of nearest neighbor searching of image features in both accuracy and efficiency compared to the classic BBF and LSH algorithms, which in turn makes it particularly suitable for image matching and image retrieval.

源语言英语
页(从-至)494-502+510
期刊Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics
22
3
出版状态已出版 - 3月 2010

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