Rule-based video classification system for basketball video indexing

Current information and communication technologies provide the infrastructure to send bits anywhere, but do not presume to handle information at the semantic level. This paper investigates the use of video content analysis and feature extraction and clustering techniques for further video semantic classifications and a supervised rule based video classification system is proposed. This system can be applied to the applications such as on-line video indexing, filtering and video summaries, etc. As an experiment, basketball video structure will be examined and categorized into different classes according to distinct visual and motional characteristics features by rule-based classifier. The semantics classes, the visual/motional feature descriptors and their statistical relationship are then studied in detail and experiment results based on basketball video will be provided and analyzed.