Joint and Individual Feature Fusion Hashing for Multi-modal Retrieval-Reference-Cited by-同舟云学术

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Joint and Individual Feature Fusion Hashing for Multi-modal Retrieval

Published:2022-12-27 Issue: Volume: Page:
ISSN:1866-9956
Container-title:Cognitive Computation
language:en
Short-container-title:Cogn Comput

Author:

Yu Jun,Zheng Yukun,Wang Yinglin,Li Zuhe,Zhu Liang

Publisher

Springer Science and Business Media LLC

Subject

Cognitive Neuroscience,Computer Science Applications,Computer Vision and Pattern Recognition

Link

https://link.springer.com/content/pdf/10.1007/s12559-022-10086-4.pdf

Reference29 articles.

1. Pouyanfar S, Yang Y, Chen SC, Shyu ML, Iyengar S. Multimedia big data analytics: a survey. ACM Comput Surv (CSUR). 2018;51(1):1–34.

2. Torralba A, Fergus R, Weiss Y. Small codes and large image databases for recognition. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition. IEEE; 2008. p. 1–8.

3. Ji R, Liu H, Cao L, Liu D, Wu Y, Huang F. Toward optimal manifold hashing via discrete locally linear embedding. IEEE Trans Image Process. 2017;26(11):5411–20.

4. Lin M, Ji R, Chen S, Sun X, Lin CW. Similarity-preserving linkage hashing for online image retrieval. IEEE Trans Image Process. 2020;29:5289–300.

5. Li S, Chen Z, Lu J, Li X, Zhou J. Neighborhood preserving hashing for scalable video retrieval. In: Proceedings of the IEEE/CVF International Conference on Computer Vision; 2019. p. 8212–21.

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1. Scalable Unsupervised Hashing via Exploiting Robust Cross-Modal Consistency;IEEE Transactions on Big Data;2024-08

2. Dance Video Motion Recognition Technology Based on Multi Feature Fusion;2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques (EASCT);2023-10-20

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