Abstract-New methods for detecting shot boundaries in video sequences and for extracting key frames using metrics based on information theory are proposed. The method for shot boundary detection relies on the mutual information (MI) and the joint entropy (JE) between the frames. It can detect cuts, fade-ins and fade-outs. The detection technique was tested on the TRECVID2003 video test set having different types of shots and containing significant object and camera motion inside the shots. It is demonstrated that the method detects both fades and abrupt cuts with high accuracy. The information theory measure provides us with better results because it exploits the inter-frame information in a more compact way than frame subtraction. It was also successfully compared to other methods published in literature. The method for key frame extraction uses MI as well. We show that it captures satisfactorily the visual content of the shot.Index Terms-Detection accuracy, entropy, key frame extraction, mutual information (MI), shot boundary detection, video analysis, video segmentation.

ABSTRACT

New methods for detecting shot boundaries in video sequences and for extracting key frames using metrics based on information theory are proposed. The method for shot cut detection relies on the mutual information and the joint entropy between the frames. It can detect cuts, fade-ins and fade-outs. The detection technique was tested on TV video sequences having different types of shots and containing significant object and camera motion inside the shots. It is demonstrated that the method detects both fades and abrupt cuts with high accuracy. The method for key frame extraction is using the mutual information. We show that it captures satisfactorily the visual content of the shot.

Abstract-A novel temporal video segmentation method that, in addition to abrupt cuts, can detect with very high accuracy gradual transitions such as dissolves, fades and wipes is proposed. The method relies on evaluating mutual information between multiple pairs of frames within a certain temporal frame window. This way we create a graph where the frames are nodes and the measures of similarity correspond to the weights of the edges. By finding and disconnecting the weak connections between nodes we separate the graph to subgraphs ideally corresponding to the shots. Experiments on TRECVID2004 video test set containing different types of shot transitions and significant object and camera motion inside the shots prove that the method is very efficient.

A new method for detecting shot boundaries in video sequences using singular value decomposition (SVD) is proposed. The method relies on performing singular value decomposition on the matrix A created from 3D histograms of single frames. We have used SVD for its capabilities to derive a low dimensional refined feature space from a high dimensional raw feature space, where pattern similarity can easily be detected. The method can detect cuts and gradual transitions, such as dissolves and fades, which cannot be detected easily by entropy measures.