C4.5: programs for machine learning
C4.5: programs for machine learning
A region—based image database system using colour and texture
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Colour Image Retrieval and Object Recognition Using the Multimodal Neighbourhood Signature
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
HMM based structuring of tennis videos using visual and audio cues
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
Building classifier ensembles for automatic sports classification
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
A novel block intensity comparison code for video classification and retrieval
Expert Systems with Applications: An International Journal
Automatic sports genre categorization and view-type classification over large-scale dataset
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Automatic video genre categorization and event detection techniques on large-scale sports data
Proceedings of the 2010 Conference of the Center for Advanced Studies on Collaborative Research
Automatic image semantic annotation based on image-keyword document model
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
A Generic Approach for Systematic Analysis of Sports Videos
ACM Transactions on Intelligent Systems and Technology (TIST)
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The problem of automatic sports video classification is considered. We develop a multistage decision making system that is founded on the concept of cues, i.e. pieces of visual evidence, characteristic of certain categories of sports that are extracted from key frames. The main decision making mechanism is a decision tree which generate hypotheses concerning the semantics of the sports video content. The final stage of the decision making process is a Hidden Markov Model system which bridges the gap between the semantic content categorisation defined by the user and the actual visual content categories. The latter is often ambiguous, as the same visual content may be attributed to different sport categories, depending on the context. We demonstrate experimentally that the contextual post-processing of the decision tree outputs by HMMs significantly improves the performance of the sports video classification system.