MSN: statistical understanding of broadcasted baseball video using multi-level semantic network

The information processing of sports video yields valuable semantics for content delivery over narrowband networks. Traditional image/video processing is formulated in terms of low-level features describing image/video structure and intensity, while the high-level knowledge such as common sense and human perceptual knowledge are encoded in abstract and nongeometric representations. The management of semantic information in video becomes more and more difficult because of the large difference in representations, levels of knowledge, and abstract episodes. This paper proposes a semantic highlight detection scheme using a Multi-level Semantic Network (MSN) for baseball video interpretation. The probabilistic structure can be applied for highlight detection and shot classification. Satisfactory results will be shown to illustrate better performance compared with the traditional ones.