Feature-based cut detection with automatic threshold selection

There has been much work concentrated on creating accurate shot boundary detection algorithms in recent years. However a truly accurate method of cut detection still eludes researchers in general. In this work we present a scheme based on stable feature tracking for inter frame differencing. Furthermore, we present a method to stabilize the differences and automatically detect a global threshold to achieve a high detection rate. We compare our scheme against other cut detection techniques on a variety of data sources that have been specifically selected because of the difficulties they present due to quick motion, highly edited sequences and computer-generated effects.

[1]  Jaron Lanier The frontier between us , 1997, CACM.

[2]  Rainer Lienhart,et al.  Reliable Transition Detection in Videos: A Survey and Practitioner's Guide , 2001, Int. J. Image Graph..

[3]  Ramin Zabih,et al.  A feature-based algorithm for detecting and classifying production effects , 1999, Multimedia Systems.

[4]  Ramin Zabih,et al.  A feature-based algorithm for detecting and classifying scene breaks , 1995, MULTIMEDIA '95.

[5]  Boon-Lock Yeo,et al.  Video visualization for compact presentation and fast browsing of pictorial content , 1997, IEEE Trans. Circuits Syst. Video Technol..

[6]  Jungwoo Lee,et al.  Multiresolution video indexing for subband coded video databases , 1994, Electronic Imaging.

[7]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Alan F. Smeaton,et al.  An evaluation of alternative techniques for automatic detection of shot boundaries in digital video , 1999 .

[9]  Wolfgang Effelsberg,et al.  Video abstracting , 1997, CACM.

[10]  Rainer Lienhart Dynamic video summarization of home video , 1999, Electronic Imaging.

[11]  Ramesh C. Jain,et al.  Production model based digital video segmentation , 1995, Multimedia Tools and Applications.

[12]  Wolfgang Effelsberg,et al.  The MoCA Project - Movie Content Analysis Research at the University of Mannheim , 1998, GI Jahrestagung.

[13]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.