Cross-media Hot Topic Auto-tracking Model Based on Semantics and Temporal Context

LIANG Meiyu; DU Junping; ZHOU Yipeng

doi:10.1049/cje.2015.07.016

Volume 24 Issue 3

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LIANG Meiyu, DU Junping, ZHOU Yipeng, “Cross-media Hot Topic Auto-tracking Model Based on Semantics and Temporal Context,” Chinese Journal of Electronics, vol. 24, no. 3, pp. 529-534, 2015, doi: 10.1049/cje.2015.07.016

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LIANG Meiyu, DU Junping, ZHOU Yipeng, “Cross-media Hot Topic Auto-tracking Model Based on Semantics and Temporal Context,” Chinese Journal of Electronics, vol. 24, no. 3, pp. 529-534, 2015, doi: 10.1049/cje.2015.07.016

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Cross-media Hot Topic Auto-tracking Model Based on Semantics and Temporal Context

doi: 10.1049/cje.2015.07.016

1.
Beijing Key Lab of Intelligent Telecommunication Software and Multimedia, School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China;
2.
School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100037, China

Funds: This work was supported by the National Basic Research Program of China (973 Program) (No.2012CB821200, No.2012CB821206), and the National Natural Science Foundation of China (No.61320106006).

More Information

Corresponding author: DU Junping (corresponding author) was born in 1963. She is now a full professor and Ph.D. tutor at the School of Computer Science and Technology, Beijing University of Posts and Telecommunications. Her research interests include artificial intelligence, image processing and pattern recognition. (Email: junpingdu@126.com)
Received Date: 2013-11-20
Rev Recd Date: 2014-06-10
Publish Date: 2015-07-10

Abstract

Abstract

As the Internet multimedia information grows explosively, seeking an automatic technology to realize the effective organization and management of crossmedia emergency information is significantly necessary. A novel cross-media hot topic auto-tracking model based on semantics and temporal context is proposed in this paper. According to the semantic correlations of cross-media information, we learn the image visual semantics by the text semantics based on the Latent Dirichlet Allocation probability model, and establish the unified cross-media information description on the same semantic level. Also a semantics-based two-step feature dimension-reduction scheme is proposed to establish the efficient semantic feature space. The self-adaptive learning of topic model is realized to track the dynamic changes in the topic. Experimental results demonstrate that the proposed method outperforms the existing methods, which further improves the effect of hot topic auto-tracking.
- Cross-media data mining,
- Topic autotracking,
- Semantic model,
- Latent dirichlet allocation (LDA),
- Temporal context

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References(11)

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