Video retrieval using high level features: exploiting query matching and confidence-based weighting

  • Authors:

  • Shi-Yong Neo;Jin Zhao;Min-Yen Kan;Tat-Seng Chua

  • Affiliations:

  • Department of Computer Science, School of Computing, National University of Singapore, Singapore;Department of Computer Science, School of Computing, National University of Singapore, Singapore;Department of Computer Science, School of Computing, National University of Singapore, Singapore;Department of Computer Science, School of Computing, National University of Singapore, Singapore

  • Venue:
  • CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
  • Year:
  • 2006

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Abstract

Recent research in video retrieval has focused on automated, high-level feature indexing on shots or frames. One important application of such indexing is to support precise video retrieval. We report on extensions of this semantic indexing on news video retrieval. First, we utilize extensive query analysis to relate various high-level features and query terms by matching the textual description and context in a time-dependent manner. Second, we introduce a framework to effectively fuse the relation weights with the detectors' confidence scores. This results in individual high level features that are weighted on a per-query basis. Tests on the TRECVID 2005 dataset show that the above two enhancements yield significant improvement in performance over a corresponding state-of-the-art video retrieval baseline.