Unsupervised Perceptual Segmentation of Natural Color Images Using Fuzzy-Based Hierarchical Algorithm

  • Junji Maeda
  • Akimitsu Kawano
  • Sato Saga
  • Yukinori Suzuki
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4522)

Abstract

This paper proposes unsupervised perceptual segmentation of natural color images using a fuzzy-based hierarchical algorithm. L  ⋆  a  ⋆  b  ⋆  color space is used to represent color features and statistical geometrical features are adopted as texture features. A fuzzy-based homogeneity measure makes a fusion of color features and texture features. Proposed hierarchical segmentation method is performed in four stages: simple splitting, local merging, global merging and boundary refinement. Experiments on segmentation of natural color images are presented to verify the effectiveness of the proposed method in obtaining perceptual segmentation.

References

  1. 1.
    Fu, K.S., Mu, J.K.: A survey on image segmentation. Pattern Recognition 13(1), 3–16 (1981)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Haralick, R.M., Shapiro, L.G.: Image segmentation techniques. Comput. Vision Graphics Image Processing 29, 100–132 (1985)CrossRefGoogle Scholar
  3. 3.
    Pal, N.R., Pal, S.K.: A review on image segmentation techniques. Pattern Recognition 26(9), 1277–1294 (1993)CrossRefGoogle Scholar
  4. 4.
    Reed, T.R., du Buf, J.M.H.: A review of recent texture segmentation and feature extraction techniques. CVGIP: Image Understanding 57, 359–372 (1993)CrossRefGoogle Scholar
  5. 5.
    Mirmehdi, M., Petrou, M.: Segmentation of color textures. IEEE Trans. Pattern Anal. Mach. Intell. 22(2), 142–159 (2000)CrossRefGoogle Scholar
  6. 6.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)CrossRefGoogle Scholar
  7. 7.
    Ma, W.Y., Manjunath, B.S.: EdgeFlow: A technique for boundary detection and image segmentation. IEEE Trans. Image Processing 9(8), 1375–1388 (2000)MATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Chen, J., Pappas, T.N., Mojsilovic, A., Rogowitz, B.E.: Adaptive perceptual color-texture image segmentation. IEEE Trans. Image Processing 14(10), 1524–1536 (2005)CrossRefGoogle Scholar
  9. 9.
    Maeda, J., Novianto, S., Saga, S., Suzuki, Y., Anh, V.V.: Rough and accurate segmentation of natural images using fuzzy region-growing algorithm. In: Proc. IEEE Int. Conf. on Image Processing, vol. 3, pp. 227–231 (1999)Google Scholar
  10. 10.
    Maeda, J., Ishikawa, C., Novianto, S., Tadehara, N., Suzuki, Y.: Rough and accurate segmentation of natural color images using fuzzy region-growing algorithm. In: Proc. 15th Int. Conf. on Pattern Recognition, vol. 3, pp. 642–645 (2000)Google Scholar
  11. 11.
    Novianto, S., Suzuki, Y., Maeda, J.: Near optimum estimation of local fractal dimension for image segmentation. Pattern Recognition Letters 24(1-3), 365–374 (2003)CrossRefGoogle Scholar
  12. 12.
    Ojala, T., Pietikäinen, M.: Unsupervised texture segmentation using feature distributions. Pattern Recognition 32(3), 477–486 (1999)CrossRefGoogle Scholar
  13. 13.
    Chen, Y.Q., Nixon, M.S., Thomas, D.W.: Statistical geometrical features for texture classification. Pattern Recognition 28(4), 537–552 (1995)CrossRefGoogle Scholar
  14. 14.
    Zadeh, L.A.: Fuzzy sets. Inform. Control 8, 338–353 (1965)MATHCrossRefMathSciNetGoogle Scholar
  15. 15.

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Junji Maeda
    • 1
  • Akimitsu Kawano
    • 1
  • Sato Saga
    • 1
  • Yukinori Suzuki
    • 1
  1. 1.Muroran Institute of Technology, Muroran 050-8585Japan

Personalised recommendations