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Color Image Segmentation based on 4-D Histogram using JND Color and Spatial Connectivity

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Aniket V. Gokhale, Kishor K. Bhoyar, Kishor M. Bhurchandi

Aniket V Gokhale, Kishor K Bhoyar and Kishor M Bhurchandi. Color Image Segmentation based on 4-D Histogram using JND Color and Spatial Connectivity. International Journal of Computer Applications 167(10):28-35, June 2017. BibTeX

	author = {Aniket V. Gokhale and Kishor K. Bhoyar and Kishor M. Bhurchandi},
	title = {Color Image Segmentation based on 4-D Histogram using JND Color and Spatial Connectivity},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2017},
	volume = {167},
	number = {10},
	month = {Jun},
	year = {2017},
	issn = {0975-8887},
	pages = {28-35},
	numpages = {8},
	url = {},
	doi = {10.5120/ijca2017914392},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Although there are several color image segmentation algorithms proposed in the literature, the image segmentation task still remains a challenge, due to very high computational complexity involved in finding the segments that are as close as possible to the ground truth. This paper proposes a color image segmentation algorithm based on 4-D Histogram, using JND color and spatial connectivity of pixels. This algorithm is an improved version of the algorithm proposed earlier in [22], which was based on limitations of human vision perception. In this work we have successfully addressed two major drawbacks of earlier work, that is missing connectivity of color segments and higher computational complexity of the segmentation algorithm. The 4D color histogram of the image is determined using JND color similarity threshold and connectivity of the neighboring pixels, by comparing current pixel with the previously encountered immediate 8-neighbor pixels. Initial segments are then merged using a slightly higher JND threshold by applying concept of agglomeration. The proposed algorithm is first tested on synthetic image dataset to validate the proposed algorithm and then applied on images in the Berkeley segmentation datasets, BSD300 and BSD500. The performance of the algorithm is estimated using Probabilistic Rand Index (PRI) and Peak Signal to Noise Ratio (PSNR). The proposed algorithm successfully identifies connected segments and shows improved results over CCH and JND color histogram based segmentation algorithms in terms of PRI, PSNR and computational complexity.


  1. M.Swain and D. Ballard,”Color indexing”, International Journal of Computer Vision, Vol.7, no.1,1991.
  2. W. Hsu, T.S. Chua, and H. K. Pung, “An Integrated color-spatial approach to Content-Based Image Retrieval”, ACM Multimedia Conference, pages 305-313, 1995.
  3. Ka-Man Wong, Chun-Ho Chey, tak-Shing Liu, Lai-Man Po, “Dominant color image retrieval using merged histogram”, Circuits and Systems,ISCAS’03 Proceedings of 2003 International Symposium, Vol. 2, pp II-908 – II-911, 2003
  4. Ju Han and Kai-Kuang Ma, ”Fuzzy color Histogram and its use in color image retrieval”, IEEE Transactions on Image Processing, Vol. 11, No. 8, 2002.
  5. Cheng, H.D., Jiang, X.H., Sun, Y., Wang, J., “Color image segmentation: Advances and prospects”, Pattern Recognition 34,2259–2281, 2001.
  6. Liew, A.W., Yan, H., Law, N.F., “Image segmentation based on adaptive cluster prototype estimation”, IEEE Trans. Fuzzy Syst. 13 (4), 444–453, 2005.
  7. Pal, N.R., Pal, S.K., “A review on image segmentation techniques”, Pattern Recognition 26 (9), 1277–1294, 1993.
  8. Aghbari, Z. A., Al-Haj, R., “Hill-manipulation: An effective algorithm for color image segmentation”, Image Vision Comput. 24 (8), 894–903, 2006..
  9. Cheng, H.D., Li, J., “Fuzzy homogeneity and scale-space approach to color image segmentation”, Pattern Recognition 36, 1545–1562, 2003.
  10. Gaurav Sharma, “Digital color imaging”, IEEE Transactions on Image Processing, Vol. 6, No.7, , pp.901-932, July1997.
  11. K. M. Bhurchandi, P. M. Nawghare, A. K. Ray, “An analytical approach for sampling the RGB color space considering limitations of human vision and its application to color image analysis”,, Proceedings of ICVGIP 2000, Banglore, pp.44-49.
  12. A. C. Guyton, “A text book of medical Physiology”, W.B.Saunders company, Philadelphia, pp.784-824, (1976).
  13. A. Moghaddamzadeh and N. Bourbakis, “A fuzzy region growing approach for segmentation of color images”, Pergamon,Pattern Recognition, Vol.30,No.6, pp.867-881, 1997.
  14. Sang Ho Park, Il Dong Yun and Sang Uk Lee, “Color image segmentation based on 3-D clustering: morphological approach”, Pergamon, Pattern Recognition, Vol.44, No.8, pp.1061-1076, 1998.
  15. Liang-Kai Huang and Mao-Jiun J.Wang, “Image thresholding by minimizing the measures of fuzziness”, Pergamon,Pattern Recognition, Vol.28,No.1, pp.41-51, 1995.
  16. Raghu Krishnapuram, Hichem Frigui and olfa Nasraoui, “Fuzzy possiblistic shell clustering Algorithms and their application to boundary detection and surface approximation- part I”,IEEE Transactions on Fuzzy Systems, Vol.3,No.1, pp.29 -43, February1995.
  17. Raghu Krishnapuram, Hichem Frigui and olfa Nasraoui, “Fuzzy possiblistic shell clustering Algorithms and their application to boundary detection and surface approximation- part II”, IEEE Transactions on Fuzzy Systems, Vol.3,No.1, pp.44-60, February1995.
  18. Milind M. Mushrif, Ajoy K. Ray,”Color image segmentation:Rough-set theoretic approach”, Elsevier Pattern Recognition Letters, pp 483-493,2008.
  19. D. Martin, C. Fowlkes, D. Tal, J. Malik, “A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics”, Proceedings of IEEE International Conference on Computer Vision, 2001, pp.416–423
  20. R. Unnikrishnan, M. Hebert, “Measures of Similarity”, IEEE Workshop on Computer Vision Applications, pp. 394–400, , 2005.
  21. D. Suganthi, S. Purushothaman, ”IMRI Segmentation using echo state neural network”, International Journal of Image Processing, Volume (2):Issue (1), pp 1-9.
  22. Kishor Bhoyar & Omprakash Kakde, “COLOR IMAGE SEGMENTATION BASED ON JND COLOR HISTOGRAM” International Journal of Image Processing (IJIP) Volume(3), Issue(6) pp 283-292
  23. Megha Sahu, K.M. Bhurchandi, “Color Image Segmentation using Genetic Algorithm”, International Journal of Computer Applications (0975 – 8887) Volume 140 – No.5, April 2016 15
  24. Gargi V. Sangamnerkar, Dr. K.K.Bhoyar, “Color Image Segmentation in HSI Color Space Based on Color JND Histogram”, International Journal of Image Processing and Visual Communication ISSN (Online) 2319-1724 : Volume 2 , Issue 3 , April 2014 20
  25. Kishor*,‖Synthetic Color Image Data set for testing Image Segmentation Algorithms‖, INTERNET, URL=,* 03 Feb 2014, [03 Dec 2014]


JND threshold, 4D Color Histogram, PRI, PSNR