Combining Motion Understanding and Keyframe Image Analysis for Broadcast Video Information Extraction

We describe a robust new approach to extract semantic concept information based on explicitly encoding static image appearance features together with motion information. For high-level semantic concept identification detection in broadcast video, we trained multi-modality classifiers which combine the traditional static image features and a new motion feature analysis method (MoSIFT). The experimental result show that the combined features have solid performance for detecting a variety of motion related concepts and provide a large improvement over static image analysis features in video.