Robust regression and outlier detection
Robust regression and outlier detection
The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
Structuring home video by snippet detection and pattern parsing
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Lessons for the future from a decade of informedia video analysis research
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
Video partitioning by temporal slice coherency
IEEE Transactions on Circuits and Systems for Video Technology
Rushes video summarization by object and event understanding
Proceedings of the international workshop on TRECVID video summarization
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Rushes footage are considered as cheap gold mine with the potential for reuse in broadcasting and filmmaking industries. However, it is difficult to mine the “gold” from the rushes since usually only minimum metadata is available. This paper focuses on the structuring and indexing of the rushes to facilitate mining and retrieval of “gold”. We present a new approach for rushes structuring and indexing based on motion feature. We model the problem by a two-level Hierarchical Hidden Markov Model (HHMM). The HHMM, on one hand, represents the semantic concepts in its higher level to provide simultaneous structuring and indexing, on the other hand, models the motion feature distributions in its lower level to support the encoding of the semantic concepts. The encouraging experimental results on TRECVID′05 BBC rushes demonstrate the effectiveness of our approach.