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Fourth IEEE International Conference on Data Mining (ICDM'04)
MMSS: Multi-Modal Story-Oriented Video Summarization
Brighton, United Kingdom
November 01-November 04
ISBN: 0-7695-2142-8
Jia-Yu Pan, Carnegie Mellon University
Hyungjeong Yang, Carnegie Mellon University
Christos Faloutsos, Carnegie Mellon University
We propose multi-modal story-oriented video summarization (MMSS) which, unlike previous works that use fine-tuned, domain-specific heuristics, provides a domain-independent, graph-based framework. MMSS uncovers correlation between information of different modalities which gives meaningful story-oriented news video summaries. MMSS can also be applied for video retrieval, giving performance that matches the best traditional retrieval techniques (OKAPI and LSI), with no fine-tuned heuristics such as tf/idf.
Citation:
Jia-Yu Pan, Hyungjeong Yang, Christos Faloutsos, "MMSS: Multi-Modal Story-Oriented Video Summarization," icdm, pp.491-494, Fourth IEEE International Conference on Data Mining (ICDM'04), 2004
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