A novel unsupervised multiple feature hashing for image retrieval and indexing (MFHIRI)
Published 2022 View Full Article
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Title
A novel unsupervised multiple feature hashing for image retrieval and indexing (MFHIRI)
Authors
Keywords
Computer vision, Image indexing, Multi-view hashing, Approximate nearest neighbor search, Feature fusion, Graph theory
Journal
JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION
Volume -, Issue -, Pages 103467
Publisher
Elsevier BV
Online
2022-02-23
DOI
10.1016/j.jvcir.2022.103467
References
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- A Survey on Learning to Hash
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- Large-scale image retrieval with supervised sparse hashing
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