A semantic network modeling for understanding baseball video

The exploitation of semantic information in videos is difficult because of the large difference in representations, levels of knowledge and abstract episodes. Traditional image/video understanding and indexing is formulated in terms of low-level features describing image/video structure and intensity, while high-level knowledge such as common sense and human perceptual knowledge are encoded. This paper attempts to bridge this gap through the integration of image/video analysis algorithms with multi-level semantic network to interpret the baseball video.