Publication Type
Journal Article
Version
acceptedVersion
Publication Date
1-2024
Abstract
The most important effect of the video hashing technique is to support fast retrieval, which is benefiting from the high efficiency of binary calculation. Current video hash approaches are thus mainly targeted at learning compact binary codes to represent video content accurately. However, they may overlook the generation efficiency for hash codes, i.e., designing lightweight neural networks. This paper proposes an method, which is not only for computing compact hash codes but also for designing a lightweight deep model. Specifically, we present an MLP-based model, where the video tensor is split into several groups and multiple axial contexts are explored to separately refine them in parallel. The axial contexts are referred to as the dynamics aggregated from different axial scales, including long/middle/short-range dependencies. The group operation significantly reduces the computational cost of the MLP backbone. Moreover, to achieve compact video hash codes, three structural losses are utilized. As demonstrated by the experiment, the three structures are highly complementary for approximating the real data structure. We conduct extensive experiments on three benchmark datasets for the unsupervised video hashing task and show the superior trade-off between performance and computational cost of our EUVH to the state of the arts.
Keywords
Codes, Computational modeling, Context modeling, Data Structure, Data structures, Deep Neural Network, Feature extraction, Hash functions, Large-scale retrieval, Transformers, Video hashing
Discipline
Graphics and Human Computer Interfaces | Numerical Analysis and Scientific Computing
Research Areas
Software and Cyber-Physical Systems
Publication
IEEE Transactions on Multimedia
First Page
1
Last Page
13
ISSN
1520-9210
Identifier
10.1109/TMM.2024.3368924
Publisher
Institute of Electrical and Electronics Engineers
Citation
DUAN, Jingru; HAO, Yanbin; ZHU, Bin; CHENG, Lechao; ZHOU, Pengyuan; and WANG, Xiang.
Efficient unsupervised video hashing with contextual modeling and structural controlling. (2024). IEEE Transactions on Multimedia. 1-13.
Available at: https://ink.library.smu.edu.sg/sis_research/8723
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/TMM.2024.3368924
Included in
Graphics and Human Computer Interfaces Commons, Numerical Analysis and Scientific Computing Commons