CLC number: TP391
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2010-05-11
Cited: 9
Clicked: 8487
Ya-hong Han, Jian Shao, Fei Wu, Bao-gang Wei. Multiple hypergraph ranking for video concept detection[J]. Journal of Zhejiang University Science C, 2010, 11(7): 525-537.
@article{title="Multiple hypergraph ranking for video concept detection",
author="Ya-hong Han, Jian Shao, Fei Wu, Bao-gang Wei",
journal="Journal of Zhejiang University Science C",
volume="11",
number="7",
pages="525-537",
year="2010",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C0910453"
}
%0 Journal Article
%T Multiple hypergraph ranking for video concept detection
%A Ya-hong Han
%A Jian Shao
%A Fei Wu
%A Bao-gang Wei
%J Journal of Zhejiang University SCIENCE C
%V 11
%N 7
%P 525-537
%@ 1869-1951
%D 2010
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C0910453
TY - JOUR
T1 - Multiple hypergraph ranking for video concept detection
A1 - Ya-hong Han
A1 - Jian Shao
A1 - Fei Wu
A1 - Bao-gang Wei
J0 - Journal of Zhejiang University Science C
VL - 11
IS - 7
SP - 525
EP - 537
%@ 1869-1951
Y1 - 2010
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C0910453
Abstract: This paper tackles the problem of video concept detection using the multi-modality fusion method. Motivated by multi-view learning algorithms, multi-modality features of videos can be represented by multiple graphs. And the graph-based semi-supervised learning methods can be extended to multiple graphs to predict the semantic labels for unlabeled video data. However, traditional graphs represent only homogeneous pairwise linking relations, and therefore the high-order correlations inherent in videos, such as high-order visual similarities, are ignored. In this paper we represent heterogeneous features by multiple hypergraphs and then the high-order correlated samples can be associated with hyperedges. Furthermore, the multi-hypergraph ranking (MHR) algorithm is proposed by defining Markov random walk on each hypergraph and then forming the mixture Markov chains so as to perform transductive learning in multiple hypergraphs. In experiments on the TRECVID dataset, a triple-hypergraph consisting of visual, textual features and multiple labeled tags is constructed to predict concept labels for unlabeled video shots by the MHR framework. Experimental results show that our approach is effective.
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