University of Marburg at TRECVID 2005: Shot Boundary Detection and Camera Motion Estimation Results
Ralph Ewerth, Christian Beringer, Tobias Kopp, Michael Niebergall, Thilo Stadelmann, Bernd Freisleben · 2005
Abstract. In this paper, we summarize our results in the shot boundary task and the low-level feature task at TRECVID 2005. The low-level feature task was to retrieve the shots in which one of the following camera motion events was present: pan, tilt and zoom. An unsupervised approach to detect shot boundaries, aimed at minimizing the impact of parameter settings, is presented [4]. Frame dissimilarities are measured by motion compensated pixel differences of subsequent DC-frames and histogram intersection of DC-frames for several frame distances. A feature vector consists of the dissimilarity value and its ratio to the maximum neighbor value within a sliding window. K-means clustering is used for both cut detection and gradual transition detection. For cut detection, the best sliding window size is estimated by evaluating the clustering quality of the “cuts ” cluster for several window sizes. Furthermore, we investigate whether an ensemble of classifiers improves the cut detection performance. For this purpose, the unsupervised learning approach is extended by two classifiers: an Adaboost-based classifier and a Support Vector Machine