TY - JOUR
T1 - Modeling semantic concepts and user preferences in content-based video retrieval
AU - Chen, Shu Ching
AU - Zhao, N. A.
AU - Shyu, Mei Ling
N1 - Funding Information:
For Shu-Ching Chen, this research was supported in part by NSF EIA-0220562 and HRD-0317692. For Mei-Ling Shyu, this research was supported in part by NSF ITR (Medium) IIS-0325260. For Na Zhao, this research was supported in part by Florida International University Dissertation Year Fellowship.
Publisher Copyright:
© 2007 World Scientific Publishing Company.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2007/9/1
Y1 - 2007/9/1
N2 - In this paper, a user-centered framework is proposed for video database modeling and retrieval to provide appealing multimedia experiences on the content-based video queries. By incorporating the Hierarchical Markov Model Mediator (HMMM) mechanism, the source videos, segmented video shots, visual/audio features, semantic events, and high-level user perceptions are seamlessly integrated in a video database. With the hierarchical and stochastic design for video databases and semantic concept modeling, the proposed framework supports the retrieval for not only single events but also temporal sequences with multiple events. Additionally, an innovative method is proposed to capture the individual user's preferences by considering both the low-level features and the semantic concepts. The retrieval and ranking of video events and the temporal patterns can be updated dynamically online to satisfy individual user's interest and information requirements. Moreover, the users' feedbacks are efficiently accumulated for the offline system training process such that the overall retrieval performance can be enhanced periodically and continuously. For the evaluation of the proposed approach, a soccer video retrieval system is developed, presented, and tested to demonstrate the overall retrieval performance improvement achieved by modeling and capturing the user preferences.
AB - In this paper, a user-centered framework is proposed for video database modeling and retrieval to provide appealing multimedia experiences on the content-based video queries. By incorporating the Hierarchical Markov Model Mediator (HMMM) mechanism, the source videos, segmented video shots, visual/audio features, semantic events, and high-level user perceptions are seamlessly integrated in a video database. With the hierarchical and stochastic design for video databases and semantic concept modeling, the proposed framework supports the retrieval for not only single events but also temporal sequences with multiple events. Additionally, an innovative method is proposed to capture the individual user's preferences by considering both the low-level features and the semantic concepts. The retrieval and ranking of video events and the temporal patterns can be updated dynamically online to satisfy individual user's interest and information requirements. Moreover, the users' feedbacks are efficiently accumulated for the offline system training process such that the overall retrieval performance can be enhanced periodically and continuously. For the evaluation of the proposed approach, a soccer video retrieval system is developed, presented, and tested to demonstrate the overall retrieval performance improvement achieved by modeling and capturing the user preferences.
KW - Hierarchical Markov Model Mediator (HMMM)
KW - Multimedia database modeling
KW - content based video retrieval
KW - feedback
KW - offline training
KW - online learning
KW - semantic concept modeling
KW - semantic event
KW - temporal event pattern
KW - user preference
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U2 - 10.1142/S1793351X07000159
DO - 10.1142/S1793351X07000159
M3 - Article
AN - SCOPUS:50949090012
VL - 1
SP - 377
EP - 402
JO - International Journal of Semantic Computing
JF - International Journal of Semantic Computing
SN - 1793-351X
IS - 3
ER -