Open AccessCross‐media Hot Topic Auto‐tracking Model Based on Semantics and Temporal Context
Author(s) -
Liang Meiyu,
Du Junping,
Zhou Yipeng
Publication year - 2015
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2015.07.016
Subject(s) - context (archaeology) , computer science , semantics (computer science) , tracking (education) , artificial intelligence , psychology , programming language , history , pedagogy , archaeology
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.