Unsupervised Image Segmentation with Adaptive Archive-Based Evolutionary Multiobjective Clustering
The aim of this paper is to propose and apply state-of-the-art multiobjective scatter search for solving image segmentation problem. The algorithm incorporates the concepts of Pareto dominance, external archiving, diversification and intensification of solutions. The multiobjective optimization method is Archive-based Hybrid Scatter Search (AbYSS) for image segmentation. It utilized fuzzy clustering method with optimization of two fitness functions, viz., the global fuzzy compactness of the clusters and the fuzzy separation. We have tested the methods on two types of grey scale images, namely SAR (synthetic aperture radar) image and CT scan (Computer Tomography) image. We then compared it with fuzzy c-means (FCM) and a popular evolutionary multiobjective evolutionary clustering named NSGA-II. The performance result for the proposed method is compatible with the existing methods.
KeywordsMultiobjective clustering soft computing
- Gonzalez, R.C., Woods, R.E.: Digital Image processing. Prentice-Hall, Englewood Cliffs (2007)Google Scholar
- Lemarechal, C., Fjortoft, R., Marthon, P., Cubero-castan, E., Lopes, A.: SAR image segmentation by morphological methods. In: Proc. SPIE, vol. 3497, pp. 111–121 (1998)Google Scholar
- Nebro, A.J., Luna, F., Alba, E., Dorronsoro, B., Durillo, J.J., Beham, A.: AbYSS: Adapting Scatter Search to Multiobjective Optimization. IEEE Transactions on Evo. Comp. 12(4) (2008)Google Scholar
- Bezdek, J.C.: Cluster validity with fuzzy sets. Cybernetics and Systems, 58–73 (1974)Google Scholar
- Saha, S., Bandyopadhyay, S.: Unsupervised pixel classification in satellite imagery using a new multiobjective symmetry based clustering approach. In: TENCON IEEE Region 10 Conference (2008)Google Scholar