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Comparative Histogram: A Spatial-Temporal Segmentation Algorithm for Video Object Segmentation

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Soft Computing as Transdisciplinary Science and Technology

Part of the book series: Advances in Soft Computing ((AINSC,volume 29))

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Abstract

A spatial-temporal segmentation algorithm based on comparative histogram for video object segmentation is proposed in this paper. First, a comparative histogram algorithm based on hierarchical distributed genetic algorithm is used to color segmentation. Next, moving regions are identified by a motion detection method, which is developed based on the several consecutive frame differences to circumvent the motion estimation complexity for the whole frame. At the third step, color segmentation and temporal segmentation results are integrated to obtain video object initial mask. Moreover, post-processing is used to eliminate these noise regions and to filter out the ragged boundary. The proposed algorithm is evaluated for several typical MPEG-4 test sequences. Experimental results show that this algorithm can give accurate object masks and object boundaries throughout the entire test sequences.

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References

  1. D. Wang (Sept. 1998) “Unsupervised video segmentation based on watersheds and temporal tracking,” IEEE Trans. Circuits Syst. Video Technol., vol. 8, pp. 539–546.

    Article  Google Scholar 

  2. Shao. Yichen, Yu. Wenhuang, and Liang. Geechen (May. 2003) “Predictive watershed: a fast watershed algorithm for video segmentation,” IEEE Trans. Circuits Syst. Video Technol., vol. 13, pp. 453–461.

    Article  Google Scholar 

  3. R. Mech and M. Wollborn (Apr. 1998) “A noise robust method for 2D shape estimation of moving objects in video sequences considering a moving camera,” Signal Processing, vol. 66, pp. 203–217.

    Article  MATH  Google Scholar 

  4. A. Neri, S. Colonnese, G. Russo, and P. Talone (Apr. 1998) “Automatic moving object and background separation,” ignal Processing, vol. 66, pp. 219–232.

    Article  MATH  Google Scholar 

  5. L. Vincent and P. Soille (June 1991) “Watersheds in digital spaces: an efficient algorithm based on immersion simulations,” IEEE Trans. Pattern Anal. Machine Intell., vol. 13, pp. 583–598.

    Article  Google Scholar 

  6. H.C. Peng et al., →Hierarchical Genetic Image Segmentation Algorithm Based on Histogram Dichotomy,↓ Electronic Letters, vol. 36, no. 10, 2000, pp. 872–874.

    Article  Google Scholar 

  7. A. Cohen (1996) “Parallel algorithm for gray scale image segmentation,” Proc, Australian and New Zealand Conf. Intelligent Information Systems, ANZIIS-96, Adelaide, Nov 18–20, pp 143–146.

    Google Scholar 

  8. J. Ohm, Ed. (May 1998) “Core Experiments on Multifunctional and Advanced Layered Coding Aspects of MPEG-4 Video,” Doc. ISO/IE JTC1/SC29/WG11 N2176.

    Google Scholar 

  9. Changick Kim and Jenq-Neng Hwang (Feb. 2002) “Fast and Automatic Video Object Segmentation and Tracking for Content-Based Applications,” IEEE Transactions on Circuits and Systems for Video Technology, VOL. 12, NO. 2, pp. 122–129.

    Article  Google Scholar 

  10. Ju Guo, Jongwon Kim and C.-C. Jay Kuo, “Fast and robust moving object segmentation technique for MPEG-4 object-based coding and functionality”.

    Google Scholar 

  11. A. Murat Tekalp (May. 1998) “Digital Video Processing,” Prentice Hall PTR., pp. 199–202.

    Google Scholar 

  12. Haralick, Robert M., and Linda G. Shapiro (1992) “Algorithms in Computer and Robot Vision,” Volume I. Addison-Wesley, 1992. pp. 28–48.

    Google Scholar 

  13. Zhao Hong-jun (Feb. 2001) “Background Motion Evaluation and Moving Object Detection,” Southwest Institute for Ethnic Groups, Vol. 27, No. 1.

    Google Scholar 

  14. M. Wollborn and R. Mech (Mar. 1998) “Refined procedure for objective evaluation of video generation algorithms,” ISO/IEC JTC1/SC29/WG11 M3448.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Information and Science Department, Information Engineering University, Zhengzhou, 450002, china

    Da-wei Su, Li-li Zhou & Ji-fang Wang

Authors
  1. Da-wei Su
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  2. Li-li Zhou
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  3. Ji-fang Wang
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Editor information

Editors and Affiliations

  1. Dept. of Computer Science, Oklahoma State University, North Greenwood Avenue 700, 74106, Tulsa, USA

    Ajith Abraham

  2. Dept. of Computer Science and Systems Engineering, Muroran Institute of Technology, Mizumoto-cho 27-1, 050-8585, Muroran, Hokkaido, Japan

    Yasuhiko Dote

  3. Dept. of Computational Science and Engineering, Nagoya University, Furo-cho, Chikusa-ku, 464-8601, Nagoya, Aichi-ken, Japan

    Takeshi Furuhashi

  4. Institut für Sicherheits-/Prüftechnik, Fraunhofer IPK Berlin, Pascalstr. 8-9, 10587, Berlin, Germany

    Mario Köppen

  5. Division of Complex Systems Technology Graduate School of Engineering, University of Hokkaido, N-13, W-8 060 KITA-KU, Sapporo, Japan

    Azuma Ohuchi

  6. PRESTO Japan Science & Technology Corp. GSSM, University of Tsukuba, 3-29-1 Otsuka, Bunkyo-ku, 12-0012, Tokyo, Japan

    Yukio Ohsawa

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© 2005 Springer-Verlag Berlin Heidelberg

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Su, Dw., Zhou, Ll., Wang, Jf. (2005). Comparative Histogram: A Spatial-Temporal Segmentation Algorithm for Video Object Segmentation. In: Abraham, A., Dote, Y., Furuhashi, T., Köppen, M., Ohuchi, A., Ohsawa, Y. (eds) Soft Computing as Transdisciplinary Science and Technology. Advances in Soft Computing, vol 29. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32391-0_22

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  • DOI: https://doi.org/10.1007/3-540-32391-0_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25055-5

  • Online ISBN: 978-3-540-32391-4

  • eBook Packages: EngineeringEngineering (R0)

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