Robust Color Image Multi-thresholding Using Between-Class Variance and Cuckoo Search Algorithm

  • Venkatesan Rajinikanth
  • N. Sri Madhava Raja
  • Suresh Chandra Satapathy
Conference paper
First Online:
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 433)

Abstract

Multi-level image thresholding is a well known pre-processing procedure, commonly used in variety of image related domains. Segmentation process classifies the pixels of the image into various group based on the threshold level and intensity value. In this paper, colour image segmentation is proposed using Cuckoo Search (CS) algorithm. The performance of the proposed technique is validated with the Bacterial Forage Optimization (BFO) and Particle Swarm Optimization (PSO). The qualitative and quantitative investigation is carried out using the parameters, such as CPU time, between-class variance value and image quality measures, such as Mean Structural Similarity Index Matrix (MSSIM), Normalized Absolute Error (NAE), Structural Content (SC) and PSNR. The robustness of the implemented segmentation procedure is also verified using the image dataset smeared with the Gaussian Noise (GN) and Speckle Noise (SN). The study shows that, CS algorithm based multi-level segmentation offers better result compared with BFO and PSO.

Keywords

Color image segmentation Otsu’s function Cuckoo search Gaussian noise Speckle noise 
This is a preview of subscription content, log in to check access.

References

  1. 1.
    Larson, E. C., Chandler, D. M.: Most apparent distortion: Full-reference image quality assessment and the role of strategy, Journal of Electronic Imaging, 19 (1), Article ID 011006 (2010).Google Scholar
  2. 2.
    Ghamisi, P., Couceiro, M. S., Martins, F. M. L., and Benediktsson, J. A.: Multilevel image segmentation based on fractional-order Darwinian particle swarm optimization, IEEE Transactions on Geoscience and Remote sensing, 52(5), pp. 2382–2394, (2014).Google Scholar
  3. 3.
    Kalyani Manda, Satapathy, S. C., Rao, K. R.: Artificial bee colony based image clustering, In proceedings of the International Conference on Information Systems Design and Intelligent Applications 2012 (INDIA 2012), Advances in Intelligent and Soft Computing, 132, pp. 29–37, (2012).Google Scholar
  4. 4.
    Manickavasagam, K., Sutha, S., Kamalanand, K.: Development of Systems for Classification of Different Plasmodium Species in Thin Blood Smear Microscopic Images, Journal of Advanced Microscopy Research, 9, (2), pp. 86–92, (2014).Google Scholar
  5. 5.
    Sezgin, M., Sankar, B.: Survey over Image Thresholding Techniques and Quantitative Performance Evaluation, Journal of Electronic Imaging, 13(1), pp. 146– 165, (2004).Google Scholar
  6. 6.
    Tuba, M.: Multilevel image thresholding by nature-inspired algorithms: A short review, Computer Science Journal of Moldova, 22(3), pp. 318–338, (2014).Google Scholar
  7. 7.
    Akay, B.: A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding, Applied Soft Computing, 13 (6), pp. 3066–3091, (2013).Google Scholar
  8. 8.
    Rajinikanth, V., Sri Madhava Raja, N., Latha, K.: Optimal Multilevel Image Thresholding: An Analysis with PSO and BFO Algorithms, Aust. J. Basic & Appl. Sci., 8(9), pp. 443–454, (2014).Google Scholar
  9. 9.
    Sathya, P. D., Kayalvizhi, R.: Modified bacterial foraging algorithm based multilevel thresholding for image segmentation, Engineering Applications of Artificial Intelligence, 24, pp. 595–615, (2011).Google Scholar
  10. 10.
    Raja, N. S. M., Rajinikanth,V., Latha, K.: Otsu Based Optimal Multilevel Image Thresholding Using Firefly Algorithm, Modelling and Simulation in Engineering, vol. 2014, Article ID 794574, p. 17, (2014).Google Scholar
  11. 11.
    Rajinikanth, V., Couceiro, M. S.: RGB Histogram Based Color Image Segmentation Using Firefly Algorithm, Procedia Computer Science, 46, pp. 1449–1457, (2015). doi: 10.1016/j.procs.2015.02.064.
  12. 12.
    Abhinaya, B., Raja, N. S. M.: Solving Multi-level Image Thresholding Problem—An Analysis with Cuckoo Search Algorithm, Information Systems Design and Intelligent Applications, Advances in Intelligent Systems and Computing, 339, pp. 177–186, (2015).Google Scholar
  13. 13.
    Agrawal, S., Panda, R., Bhuyan, S., Panigrahi, B. K.: Tsallis entropy based optimal multilevel thresholding using cuckoo search algorithm, Swarm and Evolutionary Computation, 11, pp. 16–30, (2013).Google Scholar
  14. 14.
    Grgic, S., Grgic, M., Mrak. M.: Reliability of objective picture quality measures, Journal of Electrical Engineering, 55(1–2), pp. 3–10, (2004).Google Scholar
  15. 15.
    Wang, Z., Bovik, A. C., Sheikh, H. R., Simoncelli, E.P.: Image Quality Assessment: From Error VisibilitytoStructural Similarity, IEEE Transactions on Image Processing, 13(4), pp. 600– 612, (2004).Google Scholar
  16. 16.
    Otsu, N.: A Threshold selection method from Gray-Level Histograms, IEEE T. on Systems, Man and Cybernetics, 9(1), pp. 62–66, (1979).Google Scholar
  17. 17.
    Yang, X. S., Deb, S.: Cuckoo search via Lévy flights. In: Proceedings of World Congress on Nature and Biologically Inspired Computing (NaBIC 2009), pp. 210–214,. IEEE Publications, USA (2009).Google Scholar
  18. 18.
    Yang, X. S: Nature-Inspired Metaheuristic Algorithms, Luniver Press, Frome, UK, 2008.Google Scholar
  19. 19.
    Brajevic, I., Tuba, M., Bacanin, N.: Multilevel image thresholding selection based on the Cuckoo search algorithm. In: Proceedings of the 5th International Conference on Visualization, Imaging and Simulation (VIS’12), pp. 217–222, Sliema, Malta (2012).Google Scholar
  20. 20.
  21. 21.
    Oliva, D., Cuevas, E., Pajares, G., Zaldivar, D., Perez-Cisneros, M.: Multilevel Thresholding Segmentation Based on Harmony Search Optimization, Journal of Applied Mathematics, vol. 2013, Article ID 575414, p. 24, (2013).Google Scholar
  22. 22.
    Oliva, D., Cuevas, E., Pajares, G., Zaldivar, D. andOsuna, V.: A Multilevel Thresholding algorithm using electromagnetism optimization, Neurocomputing, 139, pp. 357–381, (2014).Google Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  • Venkatesan Rajinikanth
    • 1
  • N. Sri Madhava Raja
    • 1
  • Suresh Chandra Satapathy
    • 2
  1. 1.Department of Electronics and Instrumentation EngineeringSt. Joseph’s College of EngineeringChennaiIndia
  2. 2.Department of Computer Science and EngineeringAnil Neerukonda Institute of Technology and SciencesVisakhapatnamIndia

Personalised recommendations