Abstract
Swarm intelligence algorithms have been extensively used in clustering-based applications, e.g., image segmentation, which is one of the fundamental components in image analysis and pattern recognition domains. Particle swarm optimization (PSO) is among swarm intelligence algorithms that perform based on population and random search. In this paper, a hybrid algorithm based on PSO, \(k\)-means, and learning automata is proposed for image segmentation. Each particle in the proposed method has been equipped with a learning automata (LA). In fact, each particle can either update its position by PSO method or select the next position utilizing \(k\)-means approach in each iteration based on its LA. In other word, the main aim of the hybrid proposed approach was to utilize the efficiency of PSO and \(k\)-mean methods under supervision of LA. The proposed approach along with other comparative studies has been applied for segmenting standard test images. Efficiency of the proposed method has been compared with that of other methods, and experimental results show the superiority proposed algorithm.
Access this article
Rent this article via DeepDyve
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13748-014-0044-7/MediaObjects/13748_2014_44_Fig1_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13748-014-0044-7/MediaObjects/13748_2014_44_Fig2_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13748-014-0044-7/MediaObjects/13748_2014_44_Fig3_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13748-014-0044-7/MediaObjects/13748_2014_44_Fig4_HTML.jpg)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13748-014-0044-7/MediaObjects/13748_2014_44_Fig5_HTML.jpg)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13748-014-0044-7/MediaObjects/13748_2014_44_Fig6_HTML.jpg)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13748-014-0044-7/MediaObjects/13748_2014_44_Fig7_HTML.jpg)
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Gonzalez, R., Woods, R.: Digital Image Processing, 5th edn. Pearson Education India (2000)
Arora, S., Acharya, J., Verma, A., Panigrahi, K.: Multilevel thresholding for image segmentation through a fast statistical recursive algorithm. J. Pattern Recognit. Lett. 29, 119–125 (2008)
Maitra, M., Chatterjee, A.: A hybrid cooperative-comprehensive learning based PSO algorithm for image segmentation using multilevel thresholding. J. Expert Syst. Appl. 34, 1341–1350 (2008)
Mignote, M.: Segmentation by fusion of histogram-based \(K\)-means clusters in different color spaces. IEEE Trans. Image Process. 17(5), 780–787 (2008)
Yang, X., Zhao, W., Chen, Y., Fang, X.: Image segmentation with a fuzzy clustering algorithm based on Ant-Tree. J. Signal Process. 88, 2453–2462 (2008)
Kao, Y., Zahara, E., Kao, I.: A hybridized approach to data clustering. J. Expert Syst. Appl. 34, 1754–1762 (2008)
Yazdani, D., Golyari, S., Meybodi, M.: A new hybrid approach for data clustering. In: Proceeding of the Fifth International Symposium on Telecommunication, pp. 932–937, Tehran (2010)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, pp. 1942–1948, Perth (1995)
Esmin, A., Pereira, D., Araujo, F.: Study of different approach to clustering data by using the particle swarm optimization algorithm. In: IEEE Congress on Evolutionary Computation, pp. 1817–1822, Hong Kong (2008)
Narendra, K., Thathachar, M.: Learning Automata: An Overview. Prentice Hall (1989)
Meybodi, M., Beigy, H.: A note on learning automata based schemes for adaptation of BPParameters. J. Neurocomput. 48(1), 957–974 (2002)
Howell, M., Gordonand, T., Brandao, F.: Genetic learning automata for function optimization. In: Proceeding of the IEEE Transaction on Systems, Man, and Cybernetics-Part B: Cybernetics vol 32, No. 6, pp 804–815 (2002)
Hartigan, J.: An Overview of Clustering Algorithms. Wiley, New York (1975)
Tsai, C., Kao, I.: Particle swarm optimization with selective particle regeneration for data clustering. J. Expert Syst. Appl. 38, 6565–6576 (2011)
Der Merweand, D., Engelbrecht, A.: Data clustering using particle swarm optimization. In: Proceeding of the Congress on Evolutionary Computation, pp. 215–220 (2003)
Shi, Y., Eberhart, R.: A modified particle swarm optimization. In: Proceeding of the IEEE International Conference on Evolutionary Computation, pp. 69–73, Anchorage (1998)
Thathachar, M., Sastry, P.: Varieties of learning automata: an overview. In: Proceeding of the IEEE Transaction on Systems, Man, and Cybernetics-Part B: Cybernetics, vol. 32, No.6, pp 711–722 (2002)
Forgy, E.: Cluster analysis of multivariate data: efficiency vs. interpretability of classification. Biometrics 21, 768 (1965)
Yin, P.Y.: A fast scheme for optimal thresholding using genetic algorithms. Signal Process. 72, 85–95 (1999)
Yazdani, D., Arabshahi, A., Sepas-Moghaddam, A., Dehshibi, M.M.: A multilevel thresholding method for image segmentation using a novel hybrid intelligent approach. In: Proceeding of the 12th International Conference on Hybrid Intelligent Systems (HIS), pp. 137–142 (2012)
Memarsadeghi, N., O’Leary, D.P.: Classified information: the data clustering problem. IEEE Trans. Comput. Sci. Eng. 5, 54–60 (2003)
Celebi, M.E.: Improving the performance of \(k\)-means for color quantization. J. Image Vision Comput. 29, 260–271 (2010)
Pizzuti, C., Talia, D.: P-AutoClass: scalable parallel clustering for mining large data sets. IEEE Trans. Knowl. Data Eng. 15, 629–641 (2003)
Wong, A.K.C., Li, G.C.L.: Simultaneous pattern and data clustering for pattern cluster analysis. IEEE Trans. Knowl. Data Eng. 20, 911–923 (2008)
Yang, X.L., Song, Q., Zhang, W.B.: Kernel-based deterministic annealing algorithm for data clusterin. IEEE Proc. Vision Image Signal Process. 153, 557–568 (2007)
Vannoorenberghe, P., Flouzat, G.: A belief-based pixel labeling strategy for medical and satellite image segmentation. In: Proceeding of the IEEE International Conference on Fuzzy Systems, pp. 1093–1098, Vancouver, Canada (2006)
Hartigan, J.A.: An Overview of Clustering Algorithms. Wiley, New York (1975)
Blum, C., Merkle, D.: Swarm Intelligence, Introduction and Application. Natural computing series. Springer, Berlin (2008)
Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence from Natural to Artificial Systems. Oxford University Science, Oxford (1999)
Niknam, T., Taherian, E., Pourjafarian, N., Rousta, A.: An efficient hybrid algorithm based on modified imperialist competitive algorithm and \(K\)-means for data clustering. Eng. Appl. Artif. Intell. 24, 306–317 (2011)
Yin, M., Hu, Y., Yang, F., Li, X., Gu, W.: A novel hybrid \(K\)-harmonic means and gravitational search algorithm approach for clustering. Expert Syst. Appl. 38, 9319–9324 (2011)
Yang, Y., Kamel, M.S.: An aggregated clustering approach using multi-ant colonies algorithms. Pattern Recognit. 39, 1278, 1289 (2006)
Karaboga, D., Ozturk, C.: A novel clustering approach: Artificial Bee Colony (ABC) algorithm. Appl. Soft Comput. 11, 652–657 (2011)
Hammouche, K., Diaf, M., Siarry, P.: A comparative study of various meta-heuristic techniques applied to the multilevel thresholding problem. Eng. Appl. Artif. Intell. 23, 676–688 (2010)
Yin, P.: A fast scheme for optimal thresholding using genetic algorithms. Signal Process. 72, 85–95 (1999)
Hammouche, K., Diaf, M., Siarry, P.: A multilevel automatic thresholding method based on a genetic algorithm for a fast image segmentation. Comput. Vision Image Underst. 109(2), 163–175 (2008)
Chander, A., Chatterjee, A., Siarry, P.: A new social and momentum component adaptive PSO algorithm for image segmentation. Expert Syst. Appl. 38, 4998–5004 (2011)
Sun, G., Zhang, A.: A hybrid genetic algorithm and gravitational search slgorithm for image segmentation using multilevel thresholding. Pattern Recognit. Image Anal. 7887, 707–714 (2013). Lecture Note in Computer Science
Sanyal, N., Chatterjee, A., Munshi, S.: An adaptive bacterial foraging algorithm for fuzzy entropy based image segmentation. Expert Syst. Appl. 38, 15489–15498 (2011)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Sepas-Moghaddam, A., Yazdani, D. & Shahabi, J. A novel hybrid image segmentation method. Prog Artif Intell 3, 39–49 (2014). https://doi.org/10.1007/s13748-014-0044-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13748-014-0044-7