A hybridized clustering approach using particle swarm optimization for image segmentation

Fuzzy C-means algorithm (FCM) is the most widely used fuzzy partitioning method for data cluster. The K-means algorithm implements fast, however the result is less accurate clustering. In this paper describes a hybridized clustering approach for image segmentation using particle swarm optimization to improve the classical FCM algorithm. The experimental results show that the hybridized clustering approach can provide better effectiveness on experiments of image segmentation.

[1]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[2]  Peter J. Angeline,et al.  Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences , 1998, Evolutionary Programming.

[3]  Andries Petrus Engelbrecht,et al.  Data clustering using particle swarm optimization , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[4]  Andries Petrus Engelbrecht,et al.  A Color Image Quantization Algorithm Based on Particle Swarm Optimization , 2005, Informatica.

[5]  H.M. Wechsler,et al.  Digital image processing, 2nd ed. , 1981, Proceedings of the IEEE.

[6]  Ling Wang,et al.  An effective co-evolutionary particle swarm optimization for constrained engineering design problems , 2007, Eng. Appl. Artif. Intell..

[7]  J. C. Platt.  AutoAlbum: clustering digital photographs using probabilistic model merging , 2000, 2000 Proceedings Workshop on Content-based Access of Image and Video Libraries.

[8]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[9]  R. Eberhart,et al.  Fuzzy adaptive particle swarm optimization , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[10]  M. Clerc,et al.  The swarm and the queen: towards a deterministic and adaptive particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[11]  Xin Yao,et al.  Evolving artificial neural networks , 1999, Proc. IEEE.

[12]  James C. Bezdek,et al.  Fuzzy mathematics in pattern classification , 1973 .

[13]  Andries Petrus Engelbrecht,et al.  Particle swarm optimization method for image clustering , 2005, Int. J. Pattern Recognit. Artif. Intell..

[14]  Morshed U. Chowdhury,et al.  Image semantic classification by using SVM , 2003 .