A framework for video event classification by modeling temporal context of multimodal features using HMM

Hsuan Sheng Chen*, W. J. Tsai

*Corresponding author for this work

Research output: Contribution to journalArticle

6 Scopus citations

Abstract

Semantic high-level event recognition of videos is one of most interesting issues for multimedia searching and indexing. Since low-level features are semantically distinct from high-level events, a hierarchical video analysis framework is needed, i.e., using mid-level features to provide clear linkages between low-level audio-visual features and high-level semantics. Therefore, this paper presents a framework for video event classification using temporal context of mid-level interval-based multimodal features. In the framework, a co-occurrence symbol transformation method is proposed to explore full temporal relations among multiple modalities in probabilistic HMM event classification. The results of our experiments on baseball video event classification demonstrate the superiority of the proposed approach.

Original languageEnglish
Pages (from-to)285-295
Number of pages11
JournalJournal of Visual Communication and Image Representation
Volume25
Issue number2
DOIs
StatePublished - 1 Feb 2014

Keywords

  • Baseball event classification
  • Co-occurrence symbol
  • HMM
  • Interval-based multimodal feature
  • Multimedia system
  • Multivariate temporal data classification
  • Probabilistic temporal modeling
  • Video semantic analysis

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