Incremental learning of concept descriptions: A method and experimental results
Machine intelligence 11
Models of incremental concept formation
Artificial Intelligence
Experiments with Incremental Concept Formation: UNIMEM
Machine Learning
Hierarchical Model-Based Motion Estimation
ECCV '92 Proceedings of the Second European Conference on Computer Vision
On the Automated Interpretation and Indexing of American Football
ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
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This paper presents a method to classify and learn cricket shots. The procedure begins by extracting the camera motion parameters from the shots. Then the camera parameter values are converted to symbolic form and combined to generate a symbolic description that defines the trajectory of the cricket ball. The description generated is used to classify the cricket shot and to dynamically expand or update the system's knowledge of shots. The first novel aspect of this approach is that by using the camera motion parameters, a complex and diflficult process of low level image segmenting of either the batsman or the cricket ball from video images is avoided. Also the method does not require high resolution images. Another novel aspect of this work is the use of a new incremental learning algorithm that enables the system to improve and update its knowledge base. Unlike previously developed algorithms which store training instances and have simple method to prime their concept hierarchies, the incremental learning algorithm used in this work generates compact concept hierarchies and uses evidence based forgetting. The results show that the system performs well in the task of classifying four types of cricket shots.