Semantic Video Segmentation Using Probabilistic Relaxation

In this paper we propose a method for temporal segmentation of strongly structured videos on a semantic level. The proposed method is based on a naive Bayes classifier on low level visual features, followed by a two-stage probabilistic relaxation process. The first stage relaxation is on successive video frames that have been classified with the naive Bayes classifier into structural tokens and aims to improve the initial classification result. The second relaxation process is applied on successive video segments and uses knowledge from temporal relations of structural tokens that are characteristic for each broadcasting format and results so to the video segmentation on a semantic level. The experiments carried out, show that the proposed method can be successfully applied to magazine broadcastings.