Hierarchical hidden Markov model for rushes structuring and indexing

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Detail(s)

Original languageEnglish
Title of host publicationImage and Video Retrieval
Subtitle of host publication5th International Conference, CIVR 2006, Proceedings
PublisherSpringer Verlag
Pages241-250
Volume4071 LNCS
ISBN (Print)3540360182, 9783540360186
Publication statusPublished - 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4071 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Title5th International Conference on Image and Video Retrieval, CIVR 2006
PlaceUnited States
CityTempe, AZ
Period13 - 15 July 2006

Abstract

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. © Springer-Verlag Berlin Heidelberg 2006.

Citation Format(s)

Hierarchical hidden Markov model for rushes structuring and indexing. / Ngo, Chong-Wah; Pan, Zailiang; Wei, Xiaoyong.

Image and Video Retrieval: 5th International Conference, CIVR 2006, Proceedings. Vol. 4071 LNCS Springer Verlag, 2006. p. 241-250 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4071 LNCS).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review