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Binary Representation via Jointly Personalized Sparse Hashing. (arXiv:2208.14883v1 [cs.CV])
Sept. 1, 2022, 1:14 a.m. | Xiaoqin Wang, Chen Chen, Rushi Lan, Licheng Liu, Zhenbing Liu, Huiyu Zhou, Xiaonan Luo
cs.CV updates on arXiv.org arxiv.org
Unsupervised hashing has attracted much attention for binary representation
learning due to the requirement of economical storage and efficiency of binary
codes. It aims to encode high-dimensional features in the Hamming space with
similarity preservation between instances. However, most existing methods learn
hash functions in manifold-based approaches. Those methods capture the local
geometric structures (i.e., pairwise relationships) of data, and lack
satisfactory performance in dealing with real-world scenarios that produce
similar features (e.g. color and shape) with different semantic information. …
More from arxiv.org / cs.CV updates on arXiv.org
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