Hierarchical Hidden Markov Model for Rushes Structuring and Indexing

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.

• Type: Conference
• Year: 2006
• Where: CIVR
• Authors: Chong-Wah Ngo, Zailiang Pan, Xiao-Yong Wei
• Keywords: BBC Rushes, CIVR 2006, Image Analysis, Motion Feature, Rushes Structuring