A heuristic algorithm for video scene detection using shot cluster sequence analysis
Partha Pratim Mohanta, Souvik Kumar Saha, Bhabatosh Chanda · 2010
In this paper, we present a novel scheme for segmenting video data into scenes. Based on visual similarity, the shots are first classified into clusters using modified k-means algorithm. Number of optimal clusters is decided using cluster validity analysis based on Davies-Bouldin index. Each shot is assigned a tag denoting the cluster it belongs to. Thus, the video data is represented by a sequence of cluster tags. The sequence is then analyzed by introducing the concept of stable and quasi-stable state. The elements of the sequence are merged into states and isolated elements are linked with the states to generate the scenes. The scheme is free from the dependency on critical parameters and capable of handling different types of scenes.