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Performance Evaluation and Comparative Study of Color Image Segmentation Algorithm

  • Rajiv KumarEmail author
  • S. Manjunath
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 712)

Abstract

In this research paper, authors have been proposed the color image segmentation algorithm by using HSI (hue, saturation and intensity) color model. The HSI color model is used to get the color information of the given image. The boundary of the image is extracted by using edge detection algorithm, whereas, the image regions are filled where the boundaries make the closure. Both HSI color information and edge detection are applied separately and simultaneously. The color segmented image is obtained by taking the union of HSI color information and edge detection. The performance of the proposed algorithm is evaluated and compared with existing region-growing algorithm by considering three parameters, precision (P), recall (R) and F1 value. The accuracy of the proposed algorithm is also measured by using precision-recall (PR) and receiver operator characteristics (ROC) analysis. The efficiency of the proposed algorithm has been tested on more than 1500 images from UCD (University College Dublin) image dataset and other resources. The experiment results show that the efficiency of proposed algorithm is found very significant. MATLAB is used to implement the proposed algorithm.

Index Terms

Color Edge detection Image processing Image segmentation MATLAB 

Notes

Acknowledgment

The first author (R.K.) is grateful to the Management, the Dean, the HOD and all staff members of Faculty of Computing department, Botho University, Gaborone, Botswana for their valuable support. The work of the second author (S.M.) was supported and encouraged by the Management, the Director, the Dean and the Principal of BNMIT, Bangalore, India.

References

  1. 1.
    Hosea, S.P., Ranichandra, S., Rajagopal, T.K.P.: Color image segmentation an approach. Int. J. Sci. Eng. Res. 2(3) (2011). http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.301.5431&rep=rep1&type=pdf
  2. 2.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice Hall, Upper Saddle River (2008)Google Scholar
  3. 3.
    Gonzalez, R.C., et al.: Digital Image Processing using MATLAB. Pearson Education, Upper Saddle River (2004)Google Scholar
  4. 4.
    Solomon, C., Breckon, T.: Fundamentals of Digital Image Processing: A Practical Approach with Examples in MATLAB. Wiley-Blackwell, Hoboken. ISBN: 978-0-470-84472-4Google Scholar
  5. 5.
    Aly, A.A., Deris, S.B., Zaki, N.: Research review for digital image segmentation techniques. Int. J. Comput. Sci. Inf. Technol. (IJCSIT) 3(5), 99–106 (2011). doi: 10.5121/ijcsit.2011.3509 Google Scholar
  6. 6.
    Khan, W.: Image segmentation techniques: a survey. J. Image Graph. 1(4), 166–170 (2013). doi: 10.12720/joig.1.4.166-170 Google Scholar
  7. 7.
    Bora, D.J., Gupta, A.K., Khan, F.A.: Color image segmentation using an efficient fuzzy based watershed approach. Sig. Image Process.: Int. J. (SIPIJ) 6(5), 15–34 (2015). doi: 10.5121/sipij.2015.6502 Google Scholar
  8. 8.
    Burdescu, D.D., Brezovan, M., Ganea, E., Stanescu, L.: A new method for segmentation of images represented in a HSV color space. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2009. LNCS, vol. 5807, pp. 606–617. Springer, Heidelberg (2009). doi: 10.1007/978-3-642-04697-1_57 CrossRefGoogle Scholar
  9. 9.
    Estrada, F.J., Jepson, A.D.: Benchmarking image segmentation algorithms. Int. J. Comput. Vis. 85, 167–181 (2009). doi: 10.1007/s11263-009-0251-z. SpringerCrossRefGoogle Scholar
  10. 10.
    Kumar, M.J., Raj Kumar, G., Vijay Kumar Reddy, R.: Review on image segmentation techniques. Int. J. Sci. Res. Eng. Technol. (IJSRET) 3(6), 992–997 (2014)Google Scholar
  11. 11.
    Sharma, N., Mishra, M., Shrivastava, M.: Colour image segmentation techniques and issues: an approach. Int. J. Sci. Technol. Res. 1(4), 9–12 (2012)Google Scholar
  12. 12.
    Senthilkumaran, N., Rajesh, R.: Edge detection techniques for image segmentation and a survey of soft computing approaches. Int. J. Recent Trends Eng. 1(2), 250–254 (2009)Google Scholar
  13. 13.
    Srinivas, B.L., Hemalatha, Jeevan, K.A.: Edge detection techniques for image segmentation. Int. J. Innov. Res. Comput. Commun. Eng. 3(7), 288–292 (2015). ISSN (Online) 2320-9801, ISSN (Print) 2320-9798Google Scholar
  14. 14.
    Bhardwaj, S., Mittal, A.: A survey on various edge detector techniques. Procedia Technol. 4, 220–226 (2012). doi: 10.1016/j.protcy.2012.05.033. C3IT-2012, ElsevierCrossRefGoogle Scholar
  15. 15.
    Das, S.: Comparison of various edge detection technique. Int. J. Process. Image Process. Pattern Recogn IJSIP 9(2), 143–158 (2016). ISSN 2005-4254. http://dx.doi.org/10.14257/ijsip.2016.9.2.13
  16. 16.
    Iancu, A., Popescu, B., Brezovan, M., Ganea, E.: Quantitative evaluation of color image segmentation algorithms. Int. J. Comput. Sci. Appl. 8(1), 36–53 (2011). Techno mathematics Research FoundationGoogle Scholar
  17. 17.
    Zhang, H., Fritts, J.E., Goldman, S.A.: Image segmentation evaluation: a survey of unsupervised methods. Comput. Vis. Image Underst., 260–280 (2008). Elsevier. doi: 10.1016/j.cviu.2007.08.003110. http://www.elsevier.com/locate/cviu/
  18. 18.
    Cardoso, J.S., Corte Real, L.: Toward a generic evaluation of image segmentation. IEEE Trans. Image Process. 14(11), 1173–1782 (2005). doi: 10.1109/TIP.2005.854491 CrossRefGoogle Scholar
  19. 19.
    Udupa, J.K., et al.: A framework for evaluating image segmentation algorithms. Comput. Med. Imaging Graph. 30, 75–87 (2006). doi: 10.1016/j.compmedimag.2005.12.001. Elsevier. http://www.ncbi.nlm.nih.gov/pubmed/16584976/ CrossRefGoogle Scholar
  20. 20.
    Lukac, P., et al.: The evaluation criterion for color image segmentation algorithms. J. Electr. Eng. 63(1), 13–20 (2012). ISSN 1335-3632 c 2012 FEI STU. http://www.degruyter.com/view/j/jee.2012.63.issue-1/v10187-012-0002-1/v10187-012-0002-1.xml MathSciNetGoogle Scholar
  21. 21.
    Yitzhaky, Y., Pel, E.: A method for objective edge detection evaluation and detector parameter selection. IEEE Trans. Pattern Anal. Mach. Intell. 25(10), 1027–1033 (2003). doi: 10.1109/TPAMI.2003.1217608 CrossRefGoogle Scholar
  22. 22.
    Zaitoun, N.M., Aqel, M.J.: Survey on image segmentation techniques. In: International Conference on Communication, Management and Information Technology (ICCMIT 2015), vol. 65, pp. 797–806 (2015). Procedia Comput. Sci. ElsevierGoogle Scholar
  23. 23.
    University College Dublin (UCD) database: http://www.wudapt.org/create-lcz-classification
  24. 24.

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Botho UniversityGaboroneBotswana
  2. 2.BNM Institute of TechnologyBangaloreIndia

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