Length adaptive hashing for semi-supervised semantic image retrieval-Reference-Cited by-同舟云学术

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Length adaptive hashing for semi-supervised semantic image retrieval

Published:2023-01-24 Issue:24 Volume:82 Page:38165-38187
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Lei Si-chao^ORCID,Tian Xing,Ng Wing W.Y.,Gong Yue-Jiao

Funder

Guangdong Natural Science Funds for Distinguished Young Scholars

Guangdong Regional Joint Fund for Basic and Applied Research

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Link

https://link.springer.com/content/pdf/10.1007/s11042-023-14377-2.pdf

Reference51 articles.

1. Chen Z, Lu J, Feng J et al (2016) Nonlinear discrete hashing. IEEE Trans Multimed 19(1):123–135. https://doi.org/10.1109/TMM.2016.2620604

2. Chong Y, Ding Y, Yan Q (2020) Graph-based semi-supervised learning: a review. Neurocomputing 408:216–230. https://doi.org/10.1016/j.neucom.2019.12.130

3. Datar M, Immorlica N, Indyk P, Mirrokni VS (2004) Locality-sensitive hashing scheme based on p-stable distributions. In: SoCG, pp 253–262. https://doi.org/10.1145/997817.997857

4. Gilbert A, Bowden R (2015) Geometric mining: scaling geometric hashing to large datasets. In: ICCV, pp 16–24. https://doi.org/10.1109/ICCVW.2015.135

5. Gong Y, Lazebnik S, Gordo A et al (2012) Iterative quantization: a procrustean approach to learning binary codes for large-scale image retrieval. IEEE Trans Pattern Anal Mach Intell 35(12):2916–2929. https://doi.org/10.1109/TPAMI.2012.193

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