From these results we see that the performance of the classifier can be considerably improved by using additional features other than the histogram intersection. Results for cut detection Case Description Feature Subset Correct Missed FA Misclass. This section describes a novel shot representation based on a tree structure formed by clustering the frames in a shot. This tree structure allows important operations, such as similarity comparisons, to be obtained efficiently using tree matching algorithms. This tree representation is obtained through agglomerative clustering of the shot frames.
Thist figure illustrates such a representation for a shot from an action sequence.
This shot contains significant changes which can not be captured by a single keyframe. This example clearly shows the superiority of the shot tree representation. Here the objective is to obtain high confidence as to semantic content, but not to obtain a very detailed semantic analysis. For each shot, we define a vector indicating the confidence level for each pseudo-semantic class. This pyramid is created by clustering the keyframes of the tree shot representation.