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Color image segmentation using feedforward neural networks with FCM

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Abstract

This paper proposes a hybrid technique for color image segmentation. First an input image is converted to the image of CIE L*a*b* color space. The color features “a” and “b” of CIE L*a*b* are then fed into fuzzy C-means (FCM) clustering which is an unsupervised method. The labels obtained from the clustering method FCM are used as a target of the supervised feed forward neural network. The network is trained by the Levenberg-Marquardt back-propagation algorithm, and evaluates its performance using mean square error and regression analysis. The main issues of clustering methods are determining the number of clusters and cluster validity measures. This paper presents a method namely co-occurrence matrix based algorithm for finding the number of clusters and silhouette index values that are used for cluster validation. The proposed method is tested on various color images obtained from the Berkeley database. The segmentation results from the proposed method are validated and the classification accuracy is evaluated by the parameters sensitivity, specificity, and accuracy.

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References

  1. R. C. Gonzalez, R. E. Woods. Digital Image Processing, 2nd ed., Upper Saddle River, USA: Prentice Hall, 2002.

    Google Scholar 

  2. B. Sowmya, B. Sheelarani. Colour image segmentation using soft computing techniques. International Journal of Soft Computing Applications, no. 4, pp. 69–80, 2009.

    Google Scholar 

  3. N. Senthilkumaran, R. Rajesh. Edge detection techniques for image segmentation–A survey of soft computing approaches. International Journal of Recent Trends in Engineering, vol. 1, no. 2, pp. 250–254, 2009.

    Google Scholar 

  4. S. Rajasekaran, G. A. Vijayalakshmi Pai. Neural Networks, Fuzzy Logic and Genetic Algorithms: Synthesis and Applications, Upper Saddle River, USA: Prentice Hall, 2004.

    Google Scholar 

  5. K. Deshmukh, G. N. Shinde. An adaptive neuro-fuzzy system for color image segmentation. Journal of the Indian Institute of Science, vol. 86, no. 5, pp. 493–506, 2006.

    Google Scholar 

  6. B. Sowmya, B. Sheela Rani. Colour image segmentation using fuzzy clustering techniques and competitive neural network. Applied Soft Computing, vol. 11, no. 3, pp. 3170–3178, 2011.

    Article  Google Scholar 

  7. S. O. Haykin. Neural Networks and Learning Machines, Upper Saddle River, USA: Prentice Hall, 2008.

    Google Scholar 

  8. P. J. Baldevbhai, R. S. Anand. Color image segmentation for medical images using L*a*b* color space. IOSR Journal of Electronics and Communication Engineering, vol. 1, no. 2, pp. 24–45, 2012.

    Article  Google Scholar 

  9. G. Dong, M. Xie. Color clustering and learning for image segmentation based on neural networks. IEEE Transactions on Neural Networks, vol. 16, no. 4, pp. 925–936, 2005.

    Article  Google Scholar 

  10. B. Roman, Y. Kazanovich, D. Chik, V. Tikhanoff, A. Cangelos. A neural model of selective attention and object segmentation in the visual scene: An approach based on partial synchronization and star-like architecture of connections. Neural Networks, vol. 22, no. 5–6, pp. 707–719, 2009.

    Google Scholar 

  11. N. Krishnan, C. N. K. Babu, V. V. J. Rajapandian, N. R. Devaraj. A fuzzy image segmentation using feedforward neural networks with supervised learning. In Proceedings of International Conference on Cognition and Recognition, pp. 396–400, 2009.

    Google Scholar 

  12. E. Aghajari, G. Damayanti. Incorporating FCM and back propagation neural network for image segmentation. International Journal of Computer and Communication Technology, vol. 2, no. 8, pp. 121–126, 2011.

    Google Scholar 

  13. K. Z. Mao, P. Zhao, P. H. Tan. Supervised learningbased cell image segmentation for P53 immunohistochemistry. IEEE Transactions on Biomedical Engineering, vol. 53, no. 6, pp. 1153–1163, 2006.

    Article  Google Scholar 

  14. S. Shirakawa, T. Nagao. Evolutionary image segmentation based on multiobjective clustering. In Proceedings of IEEE Congress on Evolutionary Computation, IEEE, Trondheim, Norway pp. 2466–2473, 2009.

    Google Scholar 

  15. Y. Yang, S. Y. Huang. Image segmentation by fuzzy Cmeans clustering algorithm with a novel penalty term. Computing and Informatics, vol. 26, no. 1, pp. 17–31, 2007.

    MATH  Google Scholar 

  16. D. Múica-Vargas, J. F. Gallegos-Funes, A. J. Rosales-Silva. A fuzzy clustering algorithm with spatial robust estimation constraint for noisy color image segmentation. Pattern Recognition Letters, vol. 34, no. 4, pp. 400–413, 2013.

    Article  Google Scholar 

  17. M. Yambal, H. Gupta. Image segmentation using fuzzy C means clustering: A survey. International Journal of Advanced Research in Computer and Communication Engineering, vol. 2, no. 7, pp. 2927–2929, 2013.

    Google Scholar 

  18. R. Paúl, E. Aguirre, M. García-Silvente, R. Mu˜noz-Salinas. A new fuzzy based algorithm for solving stereo vagueness in detecting and tracking people. International Journal of Approximate Reasoning, vol. 53, no. 4, pp. 693–708, 2012.

    Article  Google Scholar 

  19. R. Harrabi, E. Ben Braiek. Color image segmentation based on a modified fuzzy C-means technique and statistical features. International Journal of Computational Engineering Research, vol. 2, no. 1, pp. 120–135, 2012.

    Google Scholar 

  20. H. Le Capitaine, C. Frélicot. A fast fuzzy C-means algorithm for color image segmentation. EUSFLAT-LFA 2011, Aix-les-Bains. France: Atlantis Press, pp. 1074–1081, 2011.

    Google Scholar 

  21. B. M. Wilamowski, J. D. Irwin. The Industrial Electronics Handbook: Intelligent Systems, 2nd ed., Chapter 12, Boca Raton, USA: CRC Press, pp. 12.1–12.15, 2011.

    Google Scholar 

  22. http://en.wikipedia.org/wiki/Confusion matrix.

  23. M. A. Jaffar, M. Ishtiaq, A. Hussain, A. M. Mirza. Waveletbased color image segmentation using self-organizing map neural network. In Proceedings of International Conference on Computer Engineering and Applications, IACSIT Press, Singapore, pp. 430–437, 2011.

    Google Scholar 

  24. A. Ortiz, J. M. Górriz, J. Ramírez, D. Salas-González, J. M. Llamas-Elvira. Two fully-unsupervised methods for MR brain image segmentation using SOM-based strategies. Applied Soft Computing, vol. 13, no. 5, pp. 2668–2682, 2013.

    Article  Google Scholar 

  25. S. Bhattacharyya, P. Dutta, U. Maulik, P. K. Nandi. Multilevel activation functions for true color image segmentation using a self supervised parallel self organizing neural network (PSONN) architecture: A comparative study. International Journal of Computer, Systems and Control Engineering, vol. 1, no. 8, pp. 2405–2417, 2007.

    Google Scholar 

  26. W. X. Kang, Q. Q. Yang, R. P. Liang. The comparative research on image segmentation algorithms. In Proceedings of the 1st International Workshop on Education Technology and Computer Science, IEEE, Wuhan, China, pp. 703–707, 2009.

    Google Scholar 

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Authors and Affiliations

  1. Department of Information Technology, Sri Krishna Engineering College, Chennai, 601301, India

    S. Arumugadevi

  2. Department of Computer Science & Engineering, National Engineering College (Autonomous), Kovilpatti, 628503, India

    V. Seenivasagam

Authors
  1. S. Arumugadevi
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  2. V. Seenivasagam
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Corresponding author

Correspondence to S. Arumugadevi.

Additional information

Recommended by Associate Editor De Xu

S. Arumugadevi received the B. Sc. degree in computer science from the M. S.University, India in 1997 and M.C.A. and M.Tech. degrees in information technology from M. S. University, India in 2001 and 2007, respectively. She is currently a Ph.D. candidate and she is working as assistant professor in the Department of Information Technology, Sri Krishna Engineering College, Chennai, India. She has 13 years of experience in the field of computer science.

Her research interests include image segmentation and soft computing.

ORCID iD: 0000-0001-9772-9727

V. Seenivasagam is presently a professor of computer science and engineering at National Engineering College (Autonomous), Kovilpatti, India, and has 25 years of experience in the field of computer science and engineering. He has published 23 papers in international journals, 9 papers in international conferences and 34 papers in national conferences in the fields of image processing and soft computing. He is the member of Board of Studies in the Faculty of Computer Science and Engineering at Anna University of Technology, Tirunelveli, India.

His research interests include image processing, compiler design and soft computing.

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Arumugadevi, S., Seenivasagam, V. Color image segmentation using feedforward neural networks with FCM. Int. J. Autom. Comput. 13, 491–500 (2016). https://doi.org/10.1007/s11633-016-0975-5

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  • DOI: https://doi.org/10.1007/s11633-016-0975-5

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