Integration of Visual Temporal Information and Textual Distribution Information for News Web Video Event Mining

Chengde Zhang, Xiao Wu, Mei Ling Shyu, Qiang Peng

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

News web videos exhibit several characteristics, including a limited number of features, noisy text information, and error in near-duplicate keyframes (NDK) detection. Such characteristics have made the mining of the events from news web videos a challenging task. In this paper, a novel framework is proposed to better group the associated web videos to events. First, the data preprocessing stage performs feature selection and tag relevance learning. Next, multiple correspondence analysis is applied to explore the correlations between terms and events with the assistance of visual information. Cooccurrence and visual near-duplicate feature trajectory induced from NDKs are combined to calculate the similarity between NDKs and events. Finally, a probabilistic model is proposed for news web video event mining, where both visual temporal information and textual distribution information are integrated. Experiments on the news web videos from YouTube demonstrate that the integration of visual temporal information and textual distribution information outperforms the existing methods in the news web video event mining.

Original languageEnglish (US)
Article number7317769
Pages (from-to)124-135
Number of pages12
JournalIEEE Transactions on Human-Machine Systems
Volume46
Issue number1
DOIs
StatePublished - Feb 2016

Keywords

  • Cooccurrence
  • multiple correspondence analysis (MCA)
  • near-duplicate keyframes (NDK)
  • news web video event mining
  • visual feature trajectory

ASJC Scopus subject areas

  • Human Factors and Ergonomics
  • Control and Systems Engineering
  • Signal Processing
  • Human-Computer Interaction
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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