Content-Based Scene Change Detection of Video Sequence Using Hierarchical Hidden Markov Model

  • Jong-Hyun Park
  • Soon-Young Park
  • Seong-Jun Kang
  • Wan-Hyun Cho
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2843)


This paper presents a histogram and moment-based video scene change detection technique using hierarchical Hidden Markov Models(HMMs). The proposed method extracts two types of features from wavelet-transformed images. One is the histogram difference extracted from a low-frequency subband and the other is the normalized directional moment of double wavelet differences computed from high frequency subbands. The video segmentation process consists of two steps. A histogram-based HMM is first used to segment the input video sequence into three categories: shot, cut, and gradual scene changes. In the second stage, a moment-based HMM is used to further segment the gradual changes into fades, dissolves and wipes. The experimental results show that the proposed technique is more effective in partitioning video frames than the threshold-based method.


Video Sequence Scene Change Video Segmentation Shot Boundary Detection Histogram Difference 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Tonomura, Y., Oisuji, K., Akutsu, A., Ohba, Y.: Stored Video Handling Techniques. MTT Rev. 5, 60–82 (1993)Google Scholar
  2. 2.
    Zhang, H.J., Kankanhalli, A., Smoliar, S.W.: Automatic Partitioning of Full-Motion Video. Multimedia Systems 1, 10–28 (1993)CrossRefGoogle Scholar
  3. 3.
    Shahraray, B.: Scene Change Detection and Content-Based Sampling of Video Sequences. Proceedings, Storage and Retrieval for Image and Video Databases SPIE 2419, 2–13 (1995)Google Scholar
  4. 4.
    Zhang, H.J., Low, C.Y., Smoliar, S.W.: Video Parsing and Browsing using Compressed Data. Multimedia Tools and Applications 1, 89–111 (1995)CrossRefGoogle Scholar
  5. 5.
    Patel, N.V., Sethi, I.K.: Video Shot Detection and Characterization for Video Databases. Pattern Recognition 30, 583–592 (1997)CrossRefGoogle Scholar
  6. 6.
    Yu, J., Bozdagi, G., Harrington, S.: Feature-based Hierarchical video segmentation. In: IEEE International Conference on Image Processing, vol. 2, pp. 498–501 (1997)Google Scholar
  7. 7.
    Yu, H.H., Wolf, W.: A Hierarchical Multiresolution Video Shot Transition Detection Scheme. Computer Vision and Image Understanding 75, 196–213 (1999)CrossRefGoogle Scholar
  8. 8.
    Mittal, A., Cheong, L.F., Sing, L.T.: Robust Identification of Gradual Shot-Transition Types. In: IEEE International Conference on Image Processing, vol. 2, pp. 413–416 (2002)Google Scholar
  9. 9.
    Boreczky, J.S., Rowe, L.: Comparison of Video Shot Boundary Detection Techniques In: Proceedings, SPIE 1996 (1996)Google Scholar
  10. 10.
    Boreczky, J.S., Wilcox, L.D.: A Hidden Markov Model Framework for Video Segmentation Using Audio and Image Features. In: Proceeding of the International Conference on Acoustics, Speech, and Signal Processing, vol. 6, pp. 3741–3744 (1998)Google Scholar
  11. 11.
    Wang, C., Chan, K.L., Li, S.Z.: Spatial-Frequency Analysis for Color Image Indexing and Retrieval. In: ICARCV 1998, pp. 1461–1465 (1998)Google Scholar
  12. 12.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Addison-Wesley Inc., Reading (1992)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Jong-Hyun Park
    • 1
  • Soon-Young Park
    • 2
  • Seong-Jun Kang
    • 2
  • Wan-Hyun Cho
    • 3
  1. 1.Department of Computer ScienceChonbuk National UniversityS. Korea
  2. 2.School of Information EngineeringMokpo National UniversityS. Korea
  3. 3.Department of StatisticsChonnam National UniversityS. Korea

Personalised recommendations