New enhancements to cut, fade, and dissolve detection processes in video segmentation

ABSTRACT

We present improved algorithms for cut, fade, and dissolve detection which are fundamental steps in digital video analysis. In particular, we propose a new adaptive threshold determination method that is shown to reduce artifacts created by noise and motion in scene cut detection. We also describe new two-step algorithms for fade and dissolve detection, and introduce a method for eliminating false positives from a list of detected candidate transitions. In our detailed study of these gradual shot transitions, our objective has been to accurately classify the type of transitions (fade-in, fade-out, and dissolve) and to precisely locate the boundary of the transitions. This distinguishes our work from other early work in scene change detection which tends to focus primarily on identifying the existence of a transition rather than its precise temporal extent. We evaluate our improved algorithms against two other commonly used shot detection techniques on a comprehensive data set, and demonstrate the improved performance due to our enhancements.

References

1. 1.A. M. Alattar. Detecting and compressing dissolve regions in video sequences with a DVI multimedia image compression algorithm. In Proceedings of 1993 IEEE International Symposium on Circuit and Systems, pages 13-16, 1993.
2. 2.A. M. Alattar. Detecting fade regions in uncompressed video sequences. In Proceedings of 1997 IEEE International Conference on Acoustics Speech and Signal Processing, pages 3025-3028, 1997.
3. 3.M. Ardebilian, X. Tu, and L. Chen. Improvement of shot detection methods based on dynamic threshold selection. In Proceedings of SPIE Conference on Multimedia Storage and Archiving Systems, volume 3229, DBllas, USA, Nov. 1997.
4. 4.F. Arman, A. Hsu, and M.-Y. Chiu. Image processing on compressed data for large video databases. In ACM Multimedia '9$, pages 267-272, California, July 1993.
5. 5.B. Furht, S. W. SingUlar, and H. Zhang. Video and Image Processing in Multimedia Systems. Kluwer Academic Publishers, 1995.
6. 6.L. Gu, K. Tsui, and D. Keightley. Dissolve detection in MPEG compressed video. In Proceedings of IBBE International Conference on Intelligent Processing Systems, volume 2, pages 1692-1696, 1997.
7. 7.A. Hampapur, R. Jain, and T. Weymouth. Digital video segmentation. In Proceedings of the Second ACM International Conference on Multimedia (MULTIMEDIA '94), pages 357-364, New York, Oct. 1994. ACM Press.
8. 8.A. Hanjedic, R. L. Lagendijk, and J. Biemond. A novel video parsing method with improved thresholding. In Third Annual Conference of the Advanced School for Computing and Imaging, ASCI'97, Neitherland, 1996.
9. 9.H. Kim, S.-F. Park, J. Lee, W. M. Kimg, and S. M.-H. Song. Processing of partial video data for detection of wipes. In Proceedings of SPIE, 1999.
10. 10.V. Kobla, D. DeMenthon, and D. Doermann. Special effect edit detection using Video Trails: a comparison with existing techniques. In Proceedings of SPIE conference on Storage and Retrieval for Image and Video Databases VII, Jan. 1999.
11. 11.T. C. Kuo, Y. Lin, A. L.P.Chen, S.-C. Chen, and C. Ni. Efficient shot change detection on compressed video. In Proceedings of International Workshop on Multimedia Database Management Systems, pages 101-108, 1996.
12. 12.R. Lienhart. Comparison of automatic shot boundary detection algorithms. In Proceedings of SPIE, Image and Video Processing VII, volume SPIE 3656-29, 1999.
13. 13.G. Lupatini, C. Saraceno, and R. Leonardi. Scene break detection: a comparison. In Proceedings of 8th Workshop on Continuous-Media Databases and Applications, pages 34--41, 1998.
14. 14.J. Meng, Y. Juan, and S.-F. Chang. Scene change detection in a MPEG compressed video sequence. In IS&AT/SPIE Symposium Proceedings Vol 2419, Feb. 1995.
15. 15.A. Nagasnlm and Y. Tanaka. Automatic video indexing and full-motion search for object appea rance. In Proceedings of Second Working Conference on Visual Databases Sys terns, pages 113-127. Elsevier Science Publishers B.V., 1992.
16. 16.N. V. Patel and I. K. Sethi. Compressed video processing for cut detection. In IEE Proceedings: Vision, Image and Signal Processing, volume 134, pages 315-322, Oct. 1996.
17. 17.B. T. Truong. Video genre classification based on shot segmentation. Honours Thesis, Curtis University of Technology, Western Australia, November 1999.
18. 18.I. H. Witten, A. Moffat, and T. C. Bell. Managing Gigabytes; Compressing and Indezing Documents and Images. Van Nostrand Reinhold, New York, 1994.
19. 19.B.-L. Yeo and B. Liu. Rapid scene analysis on compressed video. IEBE Transaction on Circuits and Systems for Video Technology, 2:533-544, 1995.
20. 20.R. Zabih, J. Miller, and K. Mai. A feature-based algorithm for detecting and classifying production effects. Multimedia Systems, 7(2):119-128, 1999.
21. 21.H. Zhang, A. Kankanlmlli, and S. Smoliar. Automatic partitioning of full-motion video. Multimedia System, 1:10-28, 1993.

Index Terms

• Computing methodologies

  • Artificial intelligence

    • Computer vision

      • Computer vision problems

        • Image segmentation

        • Video segmentation

      • Computer vision tasks

        • Video summarization

      • Image and video acquisition

• Information systems

  • Information retrieval

  • Information storage systems

  • Information systems applications

    • Multimedia information systems

      • Multimedia databases