Automatic dissolve and fade detection for video sequences

Due to the enormous amount of information contained in multimedia databases, the design of automatic tools to allow content-based analysis, browsing, and retrieval is of paramount importance. We present an algorithm tailored to the detection of editing effects such as dissolve and fade, which are widely used in television and movie production. A computationally inexpensive, although effective, correlation based algorithm is presented. The experimental results highlight the effectiveness of the proposed method.

[1]  Shih-Ping Liou,et al.  Automatic key-frame selection for content-based video indexing and access , 1999, Electronic Imaging.

[2]  Alessandro Neri,et al.  Automatic key frame selection using a wavelet-based approach , 1999, Optics & Photonics.

[3]  Adnan M. Alattar Detecting fade regions in uncompressed video sequences , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[4]  Rainer Lienhart,et al.  Comparison of automatic shot boundary detection algorithms , 1998, Electronic Imaging.

[5]  David S. Doermann,et al.  Special-effect edit detection using VideoTrails: a comparison with existing techniques , 1998, Electronic Imaging.

[6]  Rainer Lienhart,et al.  Reliable dissolve detection , 2001, IS&T/SPIE Electronic Imaging.

[7]  Wei Xiong,et al.  Novel technique for automatic key frame computing , 1997, Electronic Imaging.

[8]  Alessandro Neri,et al.  Synthetic summaries of video sequences using a multiresolution based key frame selection technique in a perceptually uniform color space , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[9]  Dragutin Petkovic,et al.  Content-Based Representation and Retrieval of Visual Media: A State-of-the-Art Review , 1996 .

[10]  Dragutin Petkovic,et al.  Content-based representation and retrieval of visual media: A state-of-the-art review , 1996, Multimedia Tools and Applications.

[11]  A. Murat Tekalp,et al.  Efficient Filtering and Clustering Methods for Temporal Video Segmentation and Visual Summarization , 1998, J. Vis. Commun. Image Represent..

[12]  Ba Tu Truong,et al.  Improved fade and dissolve detection for reliable video segmentation , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[13]  Warnakulasuriya Anil Chandana Fernando,et al.  Fade and dissolve detection in uncompressed and compressed video sequences , 1999, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348).

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

[15]  Boon-Lock Yeo,et al.  Rapid scene analysis on compressed video , 1995, IEEE Trans. Circuits Syst. Video Technol..

[16]  David S. Doermann,et al.  Indexing and retrieval of the MPEG compressed video , 1998, J. Electronic Imaging.