Unsupervised Image Clustering Using Central Force Optimization Algorithm Unsupervised Image Clustering Using Central Force Optimization Algorithm
Subject Areas : electrical and computer engineering
M. H. Mozafari Maref
1
,
Seyed-Hamid Zahiri
2
1 -
2 - University of Birjand
Keywords: Central force optimization image processing clustering,
Abstract :
Central Force Optimization (CFO) is a new member of heuristic algorithms which has been recently proposed and added to swarm intelligence algorithms. In this paper, an effective unsupervised image clustering technique is proposed, using CFO and called CFO-clustering. In the presented method, each probe includes the information of center of the clusters, and fitness function contains both inter-distance and intra-distance of the samples. Extensive experimental results show that the proposed CFO-clustering outperforms other similar clustering algorithms which were designed based on the evolutionary techniques.
[1] R. A. Formato, Central Force Optimization: A New Nature Inspired Computational Framework for Multidimensional Search and Optimization, Studies in Computational Intelligence 129, pp. 221-238, 2008.
[2] R. A. Formato, "Central force optimization: a new metaheuristic with applications in applied electromagnetic," Progress in Electromagnetics Research, vol. 77, no. 1, pp. 425-491, Sep. 2007.
[3] G. M. Qubati, R. A. Formato, and N. I. Dib, "Antenna benchmark performance and array synthesis using central force optimization," Microwaves, Antennas, and Propagation, IET, vol. 4, no. 5, pp. 583-592, Oct. 2010.
[4] I. E. Evangelou, D. G. Hadjimitsis, A. A. Lazakidou, and C. Clayton, "Data mining and knowledge discovery in complex image data using artificial neural networks," in Proc. Workshop on Complex Reasoning and Geographical Data, Aug. 2001.
[5] T. Lillesand and R. Keifer, Remote Sensing and Image Interpretation, John Wiley & Sons, 1994.
[6] J. A. Hartigan, Clustering Algorithms, John Wiley & Sons, 1975.
[7] U. Maulik and S. Bandyopadhyay, "Genetic algorithm-based clustering technique," J. of the Pattern Recognition, vol. 33, no. 9, pp. 1455-1465, Dec. 2000.
[8] L. Y. Tseng and S. B. Yang, "A genetic approach to the automatic clustering problem," J. of the Pattern Recognition, vol. 34, no. 3, pp. 415-424, May. 2001.
[9] D. W. van der Merwe and A. P. Engelbrecht, "Data clustering using particle swarm optimization," in Proc. of the 2003 Congress on Evolutionary Computation, vol. 1, pp. 215-220, Sep. 2003.
[10] D. L. Davies and D. W. Bouldin, "A cluster separation measure," IEEE Trans. Pattern Anal. Machine Intell, vol. 12, no. 3, pp. 234-246, Sep. 1979.
[11] S. Bandyopadhyay and U. Maulik, "Genetic clustering for automatic evolution of clusters and application to image classification," IEEE Pattern Recognition Con., vol. 1, no. 1, pp. 421-427, Oct. 2002.
[12] V. Katari and S. C. Satapathy, "Hybridized improved genetic algorithm with variable length chromosome for image clustering," IJCSNS International J. of Computer Science and Network Security, vol. 26, no. 4, pp. 135-152, Aug. 2007.
[13] D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Reading, MA: Addison-Wesley, 1989.
