Local similarity preserved hashing learning via Markov graph for efficient similarity search

Hashing, for its efficiency to nearest neighbor search in high dimensional space, has become an attractive topic in multimedia retrieval area. In this paper, an effective hashing algorithm based on markov graph has been proposed. Through constructing a stable composite affinity graph, it can preserv...

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Bibliographic Details
Published inNeurocomputing (Amsterdam) Vol. 159; pp. 144 - 150
Main Authors Liu, Hong, Jiang, Aiwen, Wang, Mingwen, Wan, Jianyi
Format Journal Article
LanguageEnglish
Published Elsevier B.V 02.07.2015
Subjects
Hashing
Markov graph
Similarity search
Spectral decomposition
Markov graph
Hashing
Spectral decomposition
Similarity search
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Summary:Hashing, for its efficiency to nearest neighbor search in high dimensional space, has become an attractive topic in multimedia retrieval area. In this paper, an effective hashing algorithm based on markov graph has been proposed. Through constructing a stable composite affinity graph, it can preserve similarity information well in the embedded subspace. Furthermore, a practical strategy has been supplied to reduce the computational complexity. Comparisons with several state-of-the-art algorithms have been done in three public datasets. The experimental results have demonstrated that the proposed method can achieve competitive performances, and afford large scale similarity search tasks.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2015.01.073