A New Hybrid Algorithm for Video Segmentation

  • K. Mahesh
  • K. Kuppusamy
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 166)


Video segmentation became popular and most important in the digital storage media. In this video segmentation technique, initially the similar shots are segmented, subsequently the track frames in every shots are assorted using the extracted objects of every frame which highly reduces the processing time. Effective video segmentation is a challenging problem in digital storage media. In this hybrid video segmentation technique, it yields the effective video segmentation results by performing intersection on the segmented results provided by both the frame difference method as well as consecutive frame intersection method. The frame difference method considers the key frame as background and it segments the dynamic objects whereas the frame difference method segments the static and dynamic objects by intersection of objects in consecutive frames. The new hybrid technique is evaluated by varying video sequences and the efficiency is analyzed by calculating the statistical measures and kappa coefficient.


Video segmentation Discrete cosine transform k-means clustering frame difference algorithm Euclidean distance 


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  1. 1.
    A Hidden Markov Model Framework for Video Segmentation Using Audio and Image Features,
  2. 2.
    Sidiropoulos, P., Mezaris, V., Kompatsiaris, I., Meinedo, H., Bugalho, M., Trancoso, I.: Video scene segmentation system using audio visual features. In: Workshop on Image Analysis for Multimedia Interactive Services, WIAMIS (2010)Google Scholar
  3. 3.
    Fathi, A., Balcan, M.F., Ren, X., Rehg, J.M.: Combining Self Training and Active Learning for Video Segmentation. In: British Machine Vision Conference (2011)Google Scholar
  4. 4.
    Erdem, Ç.E., Sankur, B.: Performance evaluation metrics for object-based video segmentation. In: Proceeding of IEEE International Conference on Image Processing (2001)Google Scholar
  5. 5.
    Khan, S., Shah, M.: Object Based Segmentation of Video Using Color, Motion and Spatial Information. In: Proceedings of the Conference on IEEE Computer Society, vol. 2(1) (2003)Google Scholar
  6. 6.
    Murmu, K., Kumar, V.: Wavelet Based Video Segmentation and Indexing. EE678 Wavelets Application Assignment (April 2005)Google Scholar
  7. 7.
    Haindl, M., Zid, P., Holub, R.: Range video segmentation. In: Proceedings of the IEEE 10th International Conference on Information Science, Signal Processing and their Applications (2010)Google Scholar
  8. 8.
    Yadav, R.K., Sharma, S., Verma, J.S.: Deformation and Improvement of Video Segmentation Based on morphology Using SSD Technique. IJCTA 2(5), 1322–1327 (2011)Google Scholar
  9. 9.
    Lezama, J., Alahari, K., Sivic, J., Laptev, I.: Track to the Future: Spatio-temporal Video Segmentation with Long range Motion Cues. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2011)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Computer Sci. and Engg.Alagappa UniversityKaraikudiIndia

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