Title | ||
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A Deep Clustering via Automatic Feature Embedded Learning for Human Activity Recognition |
Abstract | ||
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Traditional clustering algorithms are widely used for building bag-of-words (BOW) models to aggregate spatio-temporal feature points extracted from a video for human activity recognition problems. Their performances are restricted by the computational complexity which limits the number of feature points being used. In contrast, deep clustering yields good clustering performance without the limit o... |
Year | DOI | Venue |
---|---|---|
2022 | 10.1109/TCSVT.2021.3057469 | IEEE Transactions on Circuits and Systems for Video Technology |
Keywords | DocType | Volume |
Feature extraction,Clustering algorithms,Clustering methods,Visualization,Activity recognition,Vocabulary,Task analysis | Journal | 32 |
Issue | ISSN | Citations |
1 | 1051-8215 | 0 |
PageRank | References | Authors |
0.34 | 13 | 7 |
Name | Order | Citations | PageRank |
---|---|---|---|
Ting Wang | 1 | 725 | 120.28 |
Wing W. Y. Ng | 2 | 528 | 56.12 |
Jinde Li | 3 | 0 | 0.34 |
Qiuxia Wu | 4 | 103 | 9.25 |
Shuai Zhang | 5 | 43 | 9.10 |
Chris D. Nugent | 6 | 1150 | 128.39 |
Colin Shewell | 7 | 6 | 1.92 |