Unsupervised Estimation of Video Continuity Model from Large-Scale Video Archives and Its Application to Shot Boundary Detection

Video data is a sequence of video frames and their temporal continuity is an essential property of video stream. In this paper, we present a framework of constructing video continuity model from large-scale video archives with unsupervised learning. Our method estimates the similarity distribution of continuous frame pairs by applying simple assumption on the minimum duration of continuous video segments and then determines discontinuous frame pairs as outliers. The optimal estimation is pursued with measuring the separation between the estimated distribution of continuous frame pairs and that of discontinuous frame pairs. In order to verify the validity of the obtained model, the model is applied to the shot boundary detection. The results of experimental evaluation demonstrate the feasibility and the effectiveness of our method.

[1]  Irena Koprinska,et al.  Temporal video segmentation: A survey , 2001, Signal Process. Image Commun..

[2]  Christian Petersohn,et al.  Temporal video structuring for preservation and annotation of video content , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[3]  Bhabatosh Chanda,et al.  A Model-Based Shot Boundary Detection Technique Using Frame Transition Parameters , 2012, IEEE Transactions on Multimedia.

[4]  Atreyi Kankanhalli,et al.  Automatic partitioning of full-motion video , 1993, Multimedia Systems.

[5]  Li Li,et al.  A Survey on Visual Content-Based Video Indexing and Retrieval , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[6]  S. Domnic,et al.  Walsh–Hadamard Transform Kernel-Based Feature Vector for Shot Boundary Detection , 2014, IEEE Transactions on Image Processing.

[7]  Nuno Vasconcelos,et al.  Statistical models of video structure for content analysis and characterization , 2000, IEEE Trans. Image Process..

[8]  Shin'ichi Satoh,et al.  A Framework for Video Segmentation using Global and Local Features , 2013, Int. J. Pattern Recognit. Artif. Intell..

[9]  Paul Over,et al.  Evaluation campaigns and TRECVid , 2006, MIR '06.

[10]  Paul Over,et al.  Video shot boundary detection: Seven years of TRECVid activity , 2010, Comput. Vis. Image Underst..

[11]  Vasileios Mezaris,et al.  Fast shot segmentation combining global and local visual descriptors , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[12]  Alan Hanjalic,et al.  Shot-boundary detection: unraveled and resolved? , 2002, IEEE Trans. Circuits Syst. Video Technol..