Video-Segmentierung durch Analyse audiovisueller Merkmale

Based on the increasing amount of digital videos the segmentation and classification of videos is manually no more controllable. Therefore there is a need for algorithms, which are able to filter out relevant information for suitable and significant descriptions within the video material. This diploma thesis presents a system for classification of videos through analyses of audiovisual features. Such a purpose states a complex problem on arbitrary video materials because those features should be able to gather the semantic meaning of pictures and audio signals out of videos. Therefore, this thesis is limited on the scope of the video classification using scenes of the Muppet Show. Initially basic approaches and methods for a video analysis will be explained in a detailed research. After a short overview of the development of the Muppet Show, a subsequently analysis of video material shows the characteristic attributes. Based on the gained knowledge significant audiovisual features and suitable classification models will be presented, which are consulted for the development of a prototype. Finally the quality of the classification results will be evaluated using different tests. The intention is to show that visual features such as the distribution of colours as well as the segmentation of audio signals in speak, music and environmental sounds are able to capture the semantic meaning of video scenes of the Muppet Show.