An integrated music video browsing system for personalized television

Hyoung Gook Kim, Jin Young Kim, Jun Geol Baek

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


In this paper, we propose an integrated music video browsing system for personalized digital television. The system has the functions of automatic music emotion classification, automatic theme-based music classification, salient region detection, and shot classification. From audio (music) tracks, highlight detection and emotion classification are performed on the basis of information on temporal energy, timbre and tempo. For video tracks, shot detection is fulfilled to classify shots into face shots and color-based shots. Lastly automatic grouping of themes is executed on music titles and their lyrics. With a database of international music videos, we evaluate the performance of each function implemented in this paper. The experimental results show that the music browsing system achieves remarkable performances. Thus, our system can be adopted in any digital television for providing personalized services.

Original languageEnglish
Pages (from-to)776-784
Number of pages9
JournalExpert Systems With Applications
Issue number1
Publication statusPublished - 2011 Jan


  • Highlight detection
  • Music emotion classification
  • Music video browsing
  • Shot classification
  • Theme-based music classification

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence


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