Many video related applications require identification of shot transitions and their type. Several algorithms for detecting both abrupt and gradual transitions exist. However, there is no integrated and robust framework for detecting transition types accurately. We show that combing the individual works of different researchers for identifying different transition types yields poor performance. An approach is presented using several algorithms working on patterns of effective average gradient, double chromatic difference, etc., such that the approach is robust to false detection or false-alarms. A comparison with standard approaches over 3 hours of experimental data on non-trivial video classes, such as commercials, MTV, sports, etc., shows the superiority of our approach.

In this paper, we develop the concept of descriptors based on perceptual-level motion features such as time-to-collision, shot transition and temporal motion and it is shown that by including them the representational level of the video classes is significantly enhanced, e.g. violence could be detected. The temporal context cues, which had been largely neglected by present content-based retrieval (CBR) systems, are integrated into the framework. A dynamic Bayesian framework for the CBR systems which can learn the temporal structure through the fusion of all the features is designed The experimental results for more than 4 hours of videos are presented for a number of key applications like sequence identifier, highlight extraction for sports, and detecting climax or violence.