Template-Based Scene Classification for Baseball Videos Using Efficient Playfield Segmentation

A robust scene-classification algorithm is able to provide the ground truth for video abstraction and high- level events extraction. In this paper, an efficient playfield segmentation using learning Vector Quantization (IVQ) is introduced, which is able to adapt to the variations of field colors in diverse baseball videos, and then we propose a reduced filed map feature that possesses field-class concept rather than low-level feature and it can also accelerate retrieval performance. Finally, a template-based learning algorithm is proposed for scene classification without shot detection or keyframe extraction in advance. Experiments with the inside and outside tests show that our method is capable of classifying various scenes reliably.