In this paper, we address the issue of classifying video scenes which is essential in video indexing, archiving and summarization. Compared with previous methods, we emphasize the integration of multimedia features, including image, audio and speech cues. With current state-of-the-art image and audio analysis techniques, most image and audio features we can extract from videos are very low level, therefore, classifying scenes based on features from a single medium yields poor performance. We propose a maximum entropy based method for baseball scene classification in TV broadcast videos. The maximum entropy scheme is chosen because it can automatically select and fuse multimedia features from temporal contexts.
['Wei Hua', 'Mei Han', 'Yihong Gong']
Baseball scene classification using multimedia features