Video shot spectral clustering algorithm by optimized automatic cluster model selection

Spectral clustering is one of the most efficient video shot clustering algorithms. The automatic cluster model selection is still an open issue for the spectral clustering algorithm. This paper presents a video shot spectral clustering algorithm that incorporates optimized automatic cluster model selection. A distributed gauss mixture model (DGMM) is used to represent the spatial-temporal features of each shot with the model parameters used as the feature vectors for the spectral clustering. Both the DGMM and the spectral clustering measurements are used to in a globally optimized method to automatically select the number of clusters and the feature-space dimension. Tests show that the method gives better cluster model selections and clustering results.