Cross‐media Hot Topic Auto‐tracking Model Based on Semantics and Temporal Context. Issue 3 (1st July 2015)

• Record Type: Journal Article
• Authors: Liang, Meiyu; Du, Junping; Zhou, Yipeng
• Is Part Of: Chinese journal of electronics. Volume 24:Issue 3(2015)
• Page Start: 529
• Page End: 534
• Publication Date: 2015-07-01
• Subjects: emergency management -- Internet -- multimedia computing; cross media hot topic autotracking model -- semantics context -- temporal context -- Internet multimedia information -- automatic technology -- cross media emergency information -- semantic correlations -- image visual semantics -- Latent Dirichlet allocation probability model -- unified cross media information description -- semantic feature space -- self‐adaptive learning; Electronics -- Periodicals; Electronics -- China -- Periodicals; Electronics; China; Periodicals; 621.38105
• DOI: 10.1049/cje.2015.07.016
• Languages: English
• ISSNs: 1022-4653

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. An ovel 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 out performs the existing methods, which further improves the effect of hot topic auto‐tracking.