Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
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A Setting of Initial Cluster Centers and Color Image Segmentation Using Superpixels and Fuzzy C-means(FCM) Algorithm

슈퍼픽셀과 FCM을 이용한 클러스터 초기값 설정 및 칼라영상분할

  • Received : 2012.01.24
  • Accepted : 2012.04.27
  • Published : 2012.06.30
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Abstract

In this paper, a setting method of initial cluster centers and color image segmentation using superpixels and Fuzzy C-means(FCM) algorithm is proposed. Generally, the FCM can be widely used to segment color images, and an element is assigned to any cluster with each membership values in the FCM. However the algorithm has a problem of local convergence by determining the initial cluster centers. So the selection of initial cluster centers is very important, we proposed an effective method to determine the initial cluster centers using superpixels. The superpixels can be obtained by grouping of some pixels having similar characteristics from original image, and it is projected $La^*b^*$ feature space to obtain the initial cluster centers. The proposed method can be speeded up because number of superpixels are extremely smaller than pixels of original image. To evaluate the proposed method, several color images are used for computer simulation, and we know that the proposed method is superior to the conventional algorithm by the experimental results.

본 논문에서는 슈퍼픽셀과 FCM을 이용한 클러스터 초기값 설정방법과 이를 사용한 칼라영상분할을 연구한다. 클러스터링을 이용한 대표적인 칼라영상분할 방법으로 Fuzzy C-menas (FCM) 알고리즘을 많이 사용한다. FCM은 하나의 데이터가 각 클러스터에 서로 다른 소속도를 갖도록 한다. 그러나 FCM은 초기값 설정에 따라 국부적인 수렴문제가 발생한다. 따라서 초기값 설정문제는 매우 중요한데 본 연구에서는 슈퍼픽셀을 이용하여 클러스터의 초기값을 구하는 방법을 제안한다. 슈퍼픽셀은 원 영상에서 특성이 비슷한 화소들의 묶음으로 표현되는데 먼저 원 영상으로부터 슈퍼픽셀을 구하고 이를 $La^*b^*$ 칼라특징공간에 투영하여 클러스터 초기값을 구한다. 제안방법에서 슈퍼픽셀의 수는 원영상의 화소 수보다 일반적으로 매우 적어서 클러스터 초기값 설정을 위한 고속처리가 가능하다. 제안된 알고리즘의 성능평가를 위해 다양한 칼라영상을 사용하여 컴퓨터 모의실험을 수행하였으며 실험결과 제안방법이 기존방법에 비해 영상분할 성능이 우수함을 알 수 있었다.

Keywords

References

  1. Feng Ge, Song Wang, and Tiecheng liu, "New Benchmark Image Segmentation Evaluation," Journal of Electronic Imaging, Vol.16, No.3, 033011, 2007. https://doi.org/10.1117/1.2762250
  2. J. Shi and J. Malik, "Normalized Cuts and Image Segmentation," IEEE Trans. Pattern Anal. Mach. Intell., Vol.22, No.8, pp. 888-905, 2000. https://doi.org/10.1109/34.868688
  3. B. McCane, "On the Evaluation of Image Segmentation Algorithms," Proc. the Digital Image Computing: Techniques and Applications (DICTA'97) , pp. 455-464, Auckland, New Zealand 1997.
  4. Y. Zhang, "A Survey of Evaluation Methods for Image Segmentation," Pattern Recogn., Vol.29, No.8, pp. 1335-1346, 1996. https://doi.org/10.1016/0031-3203(95)00169-7
  5. N.R. Pal and S.K. Pal, "A Review on Image Segmentation Techniques," Pattern Recogn., Vol.26, No.9, pp. 1277-1294, 1993. https://doi.org/10.1016/0031-3203(93)90135-J
  6. Pedro F. Felzenszwalb and Daniel P. Huttenlocher, "Efficient Graph-Based Image Segmentation," Int. J . Comput. Vis., Vol.59, No. 2, pp. 167-181, 2004. https://doi.org/10.1023/B:VISI.0000022288.19776.77
  7. D. Comaniciu and P. Meer, "Mean shift: A Robust Approach Toward Feature Space Analysis," IEEE Trans. Pattern Anal. Mach. Intell ., Vol.24, No.5, pp. 603-619, 2002. https://doi.org/10.1109/34.1000236
  8. Byoung-Ki Jeon, Yun-Beom Jung, and Ki- Sang Hong, "Image Segmentation by Unsupervised Sparse Clustering," Pattern Recognition Letters, Vol.27, No.14, pp. 1650-1664. 2006. https://doi.org/10.1016/j.patrec.2006.03.011
  9. James C. Bezdek, Robort Ehrlich, and William Full, "FCM: The Fuzzy C-means Clustering Algorithm," Computers and Geosciencs, Vol. 10, No.2-3, pp. 191-203, 1984. https://doi.org/10.1016/0098-3004(84)90020-7
  10. H.J. Zimmermann, Fuzzy Set Theory and Its Applications, Kluwer Academic Publishers, Boston, 2001.
  11. Carl R. O. Duda, P.E. Hart, and D.G. Stork, Pattern Classification(2nd edition) , Wiley- Interscience, New York, 2000.
  12. Khang Siang Tan and Nor Ashidi Mat Isa, "Color Image Segmentation Using Histogram Thresholding-Fuzzy c-means Hybrid Approach," Pattern Recognition, Vol.44, No.1, pp. 1-15, 2011. https://doi.org/10.1016/j.patcog.2010.07.013
  13. Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi,Pascal Fua, and Sabine Susstrunk, SLIC Superpixels, EPFL Technical Report 149300, 2010.
  14. Levinshtein A., Stere A., Kutulakos K., Fleet D., Dickinson S., and Siddiqi K., "Turbopixels: Fast Superpixels Using Geometric Flows," IEEE Trans. Pattern Anal. Mach. Intell., Vol. 31, No.12, pp. 2290-2297, 2009. https://doi.org/10.1109/TPAMI.2009.96
  15. A.Vedaldi and S. Soatto, "Quick Shift and Kernel Methods for Mode Seeking," Proc. the European Conf. on Computer Vision, Vol.4, pp. 705-718, 2008.
  16. Murat Erisoglu, Nazif Calis, and Sadullah Sakallioglu, "A New Algorithm for Initial Cluster Centers in k-means Algorithm," Pattern Recognition Letters, Vol.32, No.14, pp. 1701-1705. 2011. https://doi.org/10.1016/j.patrec.2011.07.011
  17. Zhiding.Yu, Oscar C.Au, Ruobing Zou, Weiyu Yu, and Jing Tian, "An Adaptive Unsupervised Approach Toward Pixel Clustering and Color Image Segmentation," Pattern Recognition Letters, Vol.43, No.5, pp. 1889-1905, 2010. https://doi.org/10.1016/j.patcog.2009.11.015
  18. gSLIC:a Real-Time Implementation of SLIC Superpixel Segmentation, http://www.robots.ox.ac.uk/-carl/papers/gSLIC_report.pdf, 2011.
  19. 이정환, 류현종, "영상분할을 위한 슈퍼픽셀의 분류," 한국멀티미디어학회 추계학술발표대회 논문집 제14권, 제2호, pp. 143, 2011.
  20. 유창연, 곽내정, 김영길, 안재형, "칼라영상분할을 위한 경계선 보존영역 병합방법," 멀티미디어학회논문지, 제7권, 제3호, pp. 319-326, 2004.

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