Video Scene Change Detection Using the Generalized Trace available as ps or pdf . 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. We use the global color, texture and edge histograms proposed by Chen, Bouman, and Allebach as the feature vector for each frame in a shot, and we use the L1norm to describe feature distances.
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. In this section, we make use of the similarity pyramid we proposed to organize the video database.