Scene-based movie summarization via role-community networks

Chia Ming Tsai, Li Wei Kang, Chia Wen Lin, Weisi Lin

Research output: Contribution to journalArticlepeer-review

44 Citations (Scopus)

Abstract

Video summarization techniques aim at condensing a full-length video to a significantly shortened version that still preserves the major semantic content of the original video. Movie summarization, being a special class of video summarization, is particularly challenging since a large variety of movie scenarios and film styles complicate the problem. In this paper, we propose a two-stage scene-based movie summarization method based on mining the relationship between role-communities since the role-communities in earlier scenes are usually used to develop the role relationship in later scenes. In the analysis stage, we construct a social network to characterize the interactions between role-communities. As a result, the social power of each role-community is evaluated by the community's centrality value and the role communities are clustered into relevant groups based on the centrality values. In the summarization stage, a set of feasible summary combinations of scenes is identified and an information-rich summary is selected from these candidates based on social power preservation. Our evaluation results show that in at most test cases the proposed method achieves better subjective performance than attention-based and role-based summarization methods in terms of semantic content preservation for a movie summary.

Original languageEnglish
Article number6542654
Pages (from-to)1927-1940
Number of pages14
JournalIEEE Transactions on Circuits and Systems for Video Technology
Volume23
Issue number11
DOIs
Publication statusPublished - 2013
Externally publishedYes

Keywords

  • Movie analysis
  • movie summarization
  • social network analysis
  • video adaptation
  • video summarization

ASJC Scopus subject areas

  • Media Technology
  • Electrical and Electronic Engineering

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