On Video Retrieval : Content Analysis

In this paper videos are analyzed to get a content-based decription of the video. The structure of a given video is useful to index long videos eeciently and automatically. A comparison between shots gives an overview about cut frequency, cut pattern, and scene bounds. After a shot detection the shots are grouped into clusters based on their visual similarity. A time-constraint clustering procedure is used to compare only those shots that are positioned inside a time range. Shots from diierent areas of the video (e.g., begin/end) are not compared. With this cluster information that contains a list about shots and their clusters it is possible to calculate scene bounds. A labeling of all clusters gives a declaration about the cut pattern. It is easy now to distinguish a dialogue from an action scene. The nal content analysis is done by the ImageMiner system. The ImageMiner system developed at the University of Bremen of the Image Processing Department of the Center for Computing Technology realizes content-based image retrieval for still images through a novel combination of methods and techniques of computer vision and artiical intelligence. The ImageMiner system consists of three analysis modules for computer vision, namely for color, texture, and contour analysis. Additionally exists a module for object recognition. The output of the object recognition module can be indexed by a text retrieval system. Thus, concepts like forestscene may be searched for. We combine the still image analysis with the results of the video analysis in order to retrieve shots or scenes.