Association and Temporal Rule Mining for Post-Processing of Semantic Concept Detection in Video
Resource
IEEE Transactions on Multimedia 10 (2): 240-251
Journal
IEEE Transactions on Multimedia
Journal Volume
10
Journal Issue
2
Pages
240-251
Date Issued
2008
Date
2008
Author(s)
Abstract
Automatic semantic concept detection in video is important for effective content-based video retrieval and mining and has gained great attention recently. In this paper, we propose a general post-filtering framework to enhance robustness and accuracy of semantic concept detection using association and temporal analysis for concept knowledge discovery. Co-occurrence of several semantic concepts could imply the presence of other concepts. We use association mining techniques to discover such inter-concept association relationships from annotations. With discovered concept association rules, we propose a strategy to combine associated concept classifiers to improve detection accuracy. In addition, because video is often visually smooth and semantically coherent, detection results from temporally adjacent shots could be used for the detection of the current shot. We propose temporal filter designs for inter-shot temporal dependency mining to further improve detection accuracy. Experiments on the TRECVID 2005 dataset show our post-filtering framework is both efficient and effective in improving the accuracy of semantic concept detection in video. Furthermore, it is easy to integrate our framework with existing classifiers to boost their performance. © 2008 IEEE.
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Type
journal article
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