Abstract
This paper describes a clustering approach for color image segmentation using fuzzy classification principles. The method uses classification to group pixels into homogeneous regions. Both global and local information are taken into account. This is particularly helpful in taking care of small objects and local variation of color images. Color, mean and standard deviation are used as a data source. The classification is achieved by a new version of self-organizing maps algorithm . This new algorithm is equivalent to classic fuzzy C-mean algorithm (FCM) whose objective function has been modified. Code vectors that constitute centers of classes, are distributed on a regular low dimension grid. In addition, a penalization term is added to guarantee a smooth distribution of the values of the code vectors on the grid. Tests achieved on color images, followed by an automatic evaluation revealed the good performances of the proposed method.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Cocquerez, J.P., et Philipp, S.: Analyse d’images: filtrage et segmentation, Masson (1995)
Pal, S.K., et al.: A Review on Image Segmentation Techniques. Pattern Recognition 29, 1277–1294 (1993)
Haralick, R.M., Shapiro, L.G.: Image Segmentation techniques. Computer Vision Graphics Image Processing 29, 100–132 (1985)
Sahoo, P.K., et al.: A survey of thresholding techniques. Computer Vision Graphics Image Processing 41, 233–260 (1988)
Cheng, H.D., et al.: Color Image Segmentation - Advances and Prospects. Pattern Recognition 34, 2259–2281 (2001)
Cannon, R.L., Dave, J.V., Bezdek, J.C.: Efficient Imlementation of the Fuzzy C-means Clustering Algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 8(2), 249–255 (1986)
Kohonen, T.: The Self-Organizing Maps. Neurocomputing 21, 1–6 (1998)
Pascual-Marqui, R.D., et al.: Smoothly distributed Fuzzy C-means -A New Self-Organizing Map. Pattern Recognition 34, 2395–2402 (2001)
Ohta, Y.I., Kanade, T., Sakai, T.: Color Information for Region Segmentation. Computer Graphics and Image Processing 13, 222–241 (1980)
Borsotti, M., et al.: Quantative Evaluation of color image segmentation results. Pattern Recognition letters 19, 741–747 (1998)
Pietikainen, M., et al.: Accurate color discrimination with classification based on feature distributions. In: International Conference on Pattern Recognition C, pp. 833–838 (1996)
Littmann, E., Ritter, H.: Adaptive color segmentation – a comparison of neural and statistical methods. IEEE Trans. Neural Network 8(1), 175–185 (1997)
Graepel, T., Burger, M., Obermayer, K.: Self Organizing-Maps: Generalisation and new Optimisation techniques. Neurocomputing 21, 173–190 (1998)
Lui, J., Yang, Y.H.: Multi resolution color image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 16(7), 689–700 (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hachouf, F., Mezhoud, N. (2005). A Clustering Approach for Color Image Segmentation. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2005. Lecture Notes in Computer Science, vol 3708. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11558484_65
Download citation
DOI: https://doi.org/10.1007/11558484_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29032-2
Online ISBN: 978-3-540-32046-3
eBook Packages: Computer ScienceComputer Science (R0)