Journal of the Institute of Electronics Engineers of Korea SP (대한전자공학회논문지SP)

The Institute of Electronics and Information Engineers (대한전자공학회)
권호 표지

Color Image Segmentation Based on Morphological Operation and a Gaussian Mixture Model

모폴로지 연산과 가우시안 혼합 모형에 기반한 컬러 영상 분할

  • Lee Myung-Eun (Department of Electronics Engineering, Mokpo National University) ;
  • Park Soon-Young (Department of Electronics Engineering, Mokpo National University) ;
  • Cho Wan-Hyun (Department of Statistics, Chonnam National University)
  • 이명은 (목포대학교 정보전자공학) ;
  • 박순영 (목포대학교 정보전자공학) ;
  • 조완현 (전남대학교 통계학과)
  • Published : 2006.05.01
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we present a new segmentation algorithm for color images based on mathematical morphology and a Gaussian mixture model(GMM). We use the morphological operations to determine the number of components in a mixture model and to detect their modes of each mixture component. Next, we have adopted the GMM to represent the probability distribution of color feature vectors and used the deterministic annealing expectation maximization (DAEM) algorithm to estimate the parameters of the GMM that represents the multi-colored objects statistically. Finally, we segment the color image by using posterior probability of each pixel computed from the GMM. The experimental results show that the morphological operation is efficient to determine a number of components and initial modes of each component in the mixture model. And also it shows that the proposed DAEM provides a global optimal solution for the parameter estimation in the mixture model and the natural color images are segmented efficiently by using the GMM with parameters estimated by morphological operations and the DAEM algorithm.

본 논문에서는 수학적 모폴로지 연산과 가우시안 혼합 모형에 기초한 새로운 칼라 영상 분할 알고리즘을 제안한다. 우리는 혼합 모형에서 구성 성분의 수를 결정하고, 각 구성 성분의 중심값을 계산하는데 모폴로지의 연산과 라벨링 연산을 이용한다. 그리고 칼라 특징 벡터의 확률 모형으로 가우시안 혼합 모형을 사용하고, 이들의 모수 값들을 추정하는데 결정적 어닐링 EM알고리즘을 사용한다. 최종적으로 혼합 모형으로부터 계산된 사후 확률을 이용하여 칼라 영상을 분할한다. 실험 결과를 통하여 모폴로지 연산이 혼합모형의 수를 자동으로 결정하고 각 성분의 모드를 계산하는데 아주 효율적인 방법임을 보였고, 또한 결정적 어닐링 EM 알고리즘에 의하여 추정된 가우시안 혼합 모형을 사용하여 계산된 사후 확률에 의한 영상 분할 방법이 기존의 분할 알고리즘보다 정확한 분할 방법임을 보였다.

Keywords

References

  1. M.A.T. Figueiredo and A.K. Jain, 'Unsupervised learning of finite mixture models,' IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 3, pp. 381-396, 2002 https://doi.org/10.1109/34.990138
  2. O. Lezoray and H. Cardot, 'Histogram and watershed based segmentation for color images', In Proceedings of CGIV, pp. 358-362, 2002
  3. T. Geraud, P.Y. Strub and J. Darbon, 'Color Image Segmentation Based On Automatic Morphological Clustering', In the Proceedings of the IEEE ICIP, vol 3, pp. 70-73, Thessaloniki, Greece, October 2001 https://doi.org/10.1109/ICIP.2001.958053
  4. R. C. Gonzalez, R.E. Woods and S.L.. Eddins, 'Digital Image Processing Using MATLAB', Prentice Hall, pp. 345-350, 2004
  5. P. Soille, 'Morphological Image Analysis -Principles and Application', Second Edition, Springer-Verlag, New York, 2003
  6. H. Permuter, J Francos, and I.H. Jermyn, 'Gaussian mixture models of texture and colour for image database retrieval,' Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing, 2003 https://doi.org/10.1109/ICASSP.2003.1199538
  7. A. P. Dempster, N. M. Laird and D. B. Rubin, 'Maximum Likelihood from Incomplete Data via the EM algorithm,' J. Royal. Statistical Society. Ser. B, vol. 39, pp. 1-38, 1997
  8. S.H. Park, I.D. Yun, and S.U. Lee, 'Color image Segmentation Based On 3D Clustering: Morphological Approach', Pattern Recognition, Vol 31, No.9, pp. 1061-1076, 1998 https://doi.org/10.1016/S0031-3203(97)00116-7
  9. N. Ueda and R. Nakano, 'Deterministic annealing EM algorithm', Neural Networks, vol. 11, pp. 271-282, 1998 https://doi.org/10.1016/S0893-6080(97)00133-0
  10. T. Hofmann and J.M. Buhman, 'Pairwise data clustering by deterministic annealing', IEEE Transactions on PAMI, vol. 19, no. 1, pp. 1-13, 1998 https://doi.org/10.1109/34.566806