Rule-based video classification system for basketball video indexing
MULTIMEDIA '00 Proceedings of the 2000 ACM workshops on Multimedia
News video classification using SVM-based multimodal classifiers and combination strategies
Proceedings of the tenth ACM international conference on Multimedia
Association and Content-Based Retrieval
IEEE Transactions on Knowledge and Data Engineering
XRules: an effective structural classifier for XML data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Sequential association mining for video summarization
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
| Hi-index | 0.00 |
Video classification is an important step towards multimedia understanding. Most state-of-the-art approaches which apply HMM to capture the temporal information of videos have the limitation by assuming that the current state of a video depends only on the immediate previous state. Nevertheless, this assumption may not hold for videos of various categories. In this paper, we present an effective video classifier which employs the association rule mining technique to discover the actual dependence relationship between video states. The discriminatory state transition patterns mined from different video categories are then used to perform classification. Besides capturing the association between states in the time space, we also capture the association between low-level features in spatial dimension to further distinguish the semantics of videos. Experimental results show that the performance of our association rule based classifier is quite promising.