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: 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: A. Alattar, Detecting Fade Regions In Uncompressed Video Sequences, Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4, p.3025, April 21-24, 1997
• 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: Farshid Arman , Arding Hsu , Ming-Yee Chiu, Image processing on compressed data for large video databases, Proceedings of the first ACM international conference on Multimedia, p.267-272, August 02-06, 1993, Anaheim, California, United States [doi>10.1145/166266.166297]
• 5: Borko Furht , Stephen W. Smoliar , Hongjiang Zhang, Video and image processing in multimedia systems, Kluwer Academic Publishers, Norwell, MA, 1995
• 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: A. Hampapur , T. Weymouth , R. Jain, Digital video segmentation, Proceedings of the second ACM international conference on Multimedia, p.357-364, October 15-20, 1994, San Francisco, California, United States [doi>10.1145/192593.192699]
• 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: 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: 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: T. C. T. Kuo , Y. B. Lin , A. L. P. Chen , S.-C. Chen , C. Y. Ni, Efficient Shot Change Detection on Compressed Video Data, Proceedings of the 1996 International Workshop on Multi-Media Database Management Systems (IW-MMDBMS '96), p.101, August 14-16, 1996
• 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: G. Lupatini , C. Saraceno , R. Leonardi, Scene Break Detection: A Comparison, Proceedings of the Workshop on Research Issues in Database Engineering, p.34, February 23-24, 1998
• 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: Akio Nagasaka , Yuzuru Tanaka, Automatic Video Indexing and Full-Video Search for Object Appearances, Proceedings of the IFIP TC2/WG 2.6 Second Working Conference on Visual Database Systems II, p.113-127, September 30-October 03, 1991
• 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: B. T. Truong. Video genre classification based on shot segmentation. Honours Thesis, Curtis University of Technology, Western Australia, November 1999.
• 18: Ian H. Witten , Alistair Moffat , Timothy C. Bell, Managing gigabytes (2nd ed.): compressing and indexing documents and images, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1999
• 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: Ramin Zabih , Justin Miller , Kevin Mai, A feature-based algorithm for detecting and classifying production effects, Multimedia Systems, v.7 n.2, p.119-128, March 1999 [doi>10.1007/s005300050115]
• 21: HongJiang Zhang , Atreyi Kankanhalli , Stephen W. Smoliar, Automatic partitioning of full-motion video, Multimedia Systems, v.1 n.1, p.10-28, June 1993 [doi>10.1007/BF01210504]

INDEX TERMS

Primary Classification:
  H. Information Systems

Additional Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.4 Systems
          Subjects: Multimedia databases
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.1 Multimedia Information Systems
          Subjects: Video (e.g., tape, disk, DVI)

  I. Computing Methodologies
  I.4 IMAGE PROCESSING AND COMPUTER VISION

General Terms:
Algorithms, Experimentation, Measurement