Shot Boundary Detection
Indexing and retrieval of digital video is a very active research area. Temporal video segmentation is an important step in many video processing applications. The growing amount of digital video footage is driving the need for more effective methods for shot classification, summarization, efficient access, retrieval, and browsing of large video databases. Shot boundary detection is the first step towards further analysis of the video content.
Two methods for shot boundary detection have been developed.
The first approach to shot transition detection in the uncompressed image domain, we have developed, is based on the mutual information and the joint entropy between two consecutive video frames.
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. The application of these entropy-based techniques for shot cut detection was experimentally proven to be very efficient, since they produce false acceptance rates very close to zero.
The second approach to automated shot boundary detection is using singular value decomposition (SVD). We have used SVD for its capabilities to derive a refined low dimensional 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, fades and wipes. The detection technique was tested on TV video sequences having various types of shots and significant object and camera motion inside the shots. The experiments demonstrated that, by using the projected feature space we can efficiently differentiate between gradual transitions and cuts, pans, object or camera motion, while most of the methods based on histograms fail to characterize these types of video transitions.
Z. Cernekova, I. Pitas and C. Nikou, "Information theory-based shot cut/fade detection and video summarization", IEEE Transactions on Circuits and Systems for Video Technology, vol. 16, no.1, page(s): 82- 91, January 2006.
Z.Cernekova, C.Kotropoulos and I.Pitas, "Video Shot Segmentation using Singular Value Decomposition", in Proc. of 2003 IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), vol. III, pp. 181-184, Hong-Kong, April 2003 (appears also in Proc. IEEE Multimedia and Expo 2003 (ICME), pp. 301-304, Baltimore , July 2003).
Z.Cernekova, C.Kotropoulos and I.Pitas, "Video Shot Boundary Detection using Singular Value Decomposition", in Proc. of 4th European Workshop on Image Analysis for Multimedia Interactive Services(WIAMIS-2003), London, April 2003.
MOUMIR - "Models for Unified Multimedia Information Retrieval", RTN, EC
MUSCLE - “Multimedia Understanding through Semantics, Computation and LEarning” (FP6-507752)
VISNET- European Network of Excellence, funded under the European Commission IST FP6 programme
COST211 - "Redundancy Reduction Techniques and Content Analysis for Multimedia Services"