This paper reports a system developed for video browsing based on multimodal analysis. Our multimodal approach performs audio transcription for shot categorization (sports, weather, politics and economy) combining audio and visual information for theme categorization. Its main features include static and dynamic summaries, segmentation using face detection, classification into Indoor/Outdoor scenes based on Support Vector Machine (SVM) and audio transcription for theme keyword search. Keywords are selected to represent the subjects, followed by a simple text search. We conduct a set of experiments for evaluating the effectiveness of the shot subject categorization using audio transcription information.

This paper proposes a content-based video analysis system called EVEREVIS (Event Recognizer in Video Scenes), implemented as a Web-based system. In this prototype, news broadcast videos are segmented into indoor and outdoor scenes in order to facilitate navigation. Video summarization is performed and then the visual information content is characterized using HueSIFT. Support vector machine classifier is used for scenes classification and results show the plausibility of the system.