Joint scene classification and segmentation based on Hidden Markov Model

Jincheng Huang, Zhu Liu, Yao Wang

Research output: Contribution to journalArticlepeer-review

Abstract

Scene classification and segmentation are fundamental steps for efficient accessing, retrieving and browsing large amount of video data. We have developed a scene classification scheme using a Hidden Markov Model (HMM)-based classifier. By utilizing the temporal behaviors of different scene classes, HMM classifier can effectively classify presegmented clips into one of the predefined scene classes. In this paper, we describe three approaches for joint classification and segmentation based on HMM, which search for the most likely class transition path by using the dynamic programming technique. All these approaches utilize audio and visual information simultaneously. The first two approaches search optimal scene class transition based on the likelihood values computed for short video segment belonging to a particular class but with different search constrains. The third approach searches the optimal path in a super HMM by concatenating HMM's for different scene classes.

Original languageEnglish (US)
Pages (from-to)538-550
Number of pages13
JournalIEEE Transactions on Multimedia
Volume7
Issue number3
DOIs
StatePublished - Jun 2005

Keywords

  • Dynamic programming
  • Hidden Markov model
  • Video analysis
  • Video scene classification
  • Video scene segmentation
  • Video understanding

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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