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Using association rule mining to enrich semantic concepts for video retrieval

Fatemi, Nastaran and Poulin, Florian and Raileany, Laura E. and Smeaton, Alan F. (2009) Using association rule mining to enrich semantic concepts for video retrieval. In: KDIR 2009 - International Conference on Knowledge Discovery and Information Retieval, 6-8 October, 2009, Madeira, Portugal.

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In order to achieve true content-based information retrieval on video we should analyse and index video with high-level semantic concepts in addition to using user-generated tags and structured metadata like title, date, etc. However the range of such high-level semantic concepts, detected either manually or automatically, usually limited compared to the richness of information content in video and the potential vocabulary of available concepts for indexing. Even though there is work to improve the performance of individual concept classifiers, we should strive to make the best use of whatever partial sets of semantic concept occurrences are available to us. We describe in this paper our method for using association rule mining to automatically enrich the representation of video content through a set of semantic concepts based on concept co-occurrence patterns. We describe our experiments on the TRECVid 2005 video corpus annotated with the 449 concepts of the LSCOM ontology. The evaluation of our results shows the usefulness of our approach.

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Additional Information:Contact for details
Uncontrolled Keywords:mining multimedia data; association rule mining; video indexing; inter-concept relations;
Subjects:Computer Science > Machine learning
Computer Science > Artificial intelligence
Computer Science > Multimedia systems
Computer Science > Digital video
Computer Science > Information retrieval
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > CLARITY: The Centre for Sensor Web Technologies
Official URL:
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:RCSO-TIC Reserve Funds of Switzerland, Science Foundation Ireland
ID Code:4708
Deposited On:21 Jul 2009 13:08 by Alan Smeaton. Last Modified 29 Apr 2010 12:05

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