You can play that again: exploring social redundancy to derive highlight regions in videos

ABSTRACT

Identifying highlights in multimedia content such as video and audio is currently a very difficult technical problem. We present and evaluate a novel algorithm that identifies highlights by combining content analysis with Web 2.0 data mining techniques. We exploit the fact that popular content tends to be redundantly uploaded onto community sharing sites. Our "social summarization" technique first identifies overlaps in uploaded scenes and then uses the upload frequency of each video scene to compute that scene's importance in the complete video. Our user evaluation shows the reliability of the technique: scenes automatically selected by our method are agreed by experts to be the most relevant.

REFERENCES

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing
          Subjects: Abstracting methods

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Selection process

General Terms:
Algorithms, Human Factors

Keywords:
community, social network, summarization, video content analysis