Home   >   CSC-OpenAccess Library   >    Manuscript Information
Color Image Segmentation based on JND Color Histogram
Kishor K. Bhoyar, Omprakash G. Kakde
Pages - 283 - 292     |    Revised - 12-12-2009     |    Published - 12-01-2010
Volume - 3   Issue - 6    |    Publication Date - January 2010  Table of Contents
Color Image Segmentation, Just noticeable difference, JND Histogram
This paper proposes a new color image segmentation approach based on JND (Just Noticeable Difference) histogram. Histogram of the given color image is computed using JND color model. This samples each of the three axes of color space so that just enough number of visually different color bins (each bin containing visually similar colors) are obtained without compromising the visual image content. The histogram bins are further reduced using agglomeration process. This merges similar histogram bins together based on a specific threshold in terms of JND. This agglomerated histogram yields the final segmentation based on similar colors. The performance of the proposed approach is evaluated on Berkeley Segmentation Database. Two significant criterias namely PSNR and PRI (Probabilistic Rand Index) are used to evaluate the performance. Experimental results show that the proposed approach gives better results than conventional color histogram (CCH) based method and with drastically reduced time complexity.
CITED BY (25)  
1 Hettiarachchi, R., & Peters, J. F. (2016). Voronoi Region-Based Adaptive Unsupervised Color Image Segmentation. arXiv preprint arXiv:1604.00533.
2 Soeleman, M. A., Hariadi, M., Yuniarno, E. M., & Purnomo, M. H. (2015). Automatic Moving Objects Segmentation Enhancement Based on Fuzzy C-Means with Gabor Filter and Minkowski Distance. International Review on Computers and Software (IRECOS), 10(10), 1054-1061.
3 Tannous, F. K. I. Character Recognition in LEDs and LCDs.
4 Gilles, J., & Heal, K. (2014). A parameterless scale-space approach to find meaningful modes in histograms—Application to image and spectrum segmentation. International Journal of Wavelets, Multiresolution and Information Processing, 12(06), 1450044.
5 Dey, E. K., & Muctadir, H. M. (2014, May). Chest X-ray analysis to detect mass tissue in lung. In Informatics, Electronics & Vision (ICIEV), 2014 International Conference on (pp. 1-5). IEEE.
6 Chen, H., Ding, H., He, X., & Zhuang, H. (2014, October). Color image segmentation based on seeded region growing with Canny edge detection. In Signal Processing (ICSP), 2014 12th International Conference on (pp. 683-686). IEEE.
7 Tarwani, K. M., & Bhoyar, K. K. Approaches to Gender Classification using Facial Images.
8 Ha Chen, Fang Fang, Hu & trophy. (2013). Summary of fuzzy clustering algorithm. Life Science Instruments, (6), 33-37.
9 Chen Xiaojuan. (2013). Color image segmentation method based on level set of Science Technology and Engineering (23), 6756-6759.
11 Goswami, T., Agarwala, A., & Raoa, C. R. Hybrid Region and Edge Based Unsupervised Color-Texture Segmentation for Natural Images.
12 Sangamnerkar, G. V., & Bhoyar, K. K. Color Image Segmentation in HSI Color Space Based on Color JND Histogram.
13 Sivanand, S., & Raj, A. Color Image Segmentation Using MDS-Based Multiresolution Nonlinear dimensionality Reduction Model and Fuzzy C-means Clustering.
14 Janwe, N. J., & Bhoyar, K. K. (2013, December). Video shot boundary detection based on JND color histogram. In Image Information Processing (ICIIP), 2013 IEEE Second International Conference on (pp. 476-480). IEEE.
15 Tan, K. S., Isa, N. A. M., & Lim, W. H. (2013). Color image segmentation using adaptive unsupervised clustering approach. Applied Soft Computing, 13(4), 2017-2036.
16 Smaoui, N., & Bessassi, S. (2013). A developed system for melanoma diagnosis. International Journal of Computer Vision and Signal Processing, 3(1), 10-17.
17 Tan, K. S., Lim, W. H., & Isa, N. A. M. (2013). Novel initialization scheme for Fuzzy C-Means algorithm on color image segmentation. Applied Soft Computing, 13(4), 1832-1852.
18 Hunters often, Mr Wong & wind. (2012). Segmentation. Computer and Digital Engineering perceptual characteristics of color image based on, 40 (2), 92-95.
19 Chang, P., Wang, X., & Huang, J. (2012, March). Color image segmentation based on visual perception. In Information Science and Technology (ICIST), 2012 International Conference on (pp. 425-429). IEEE.
20 Soeleman, M. A., Hariadi, M., & Purnomo, M. H. (2012, November). Adaptive threshold for background subtraction in moving object detection using Fuzzy C-Means clustering. In TENCON 2012-2012 IEEE Region 10 Conference (pp. 1-5). IEEE.
21 Mignotte, M. (2012). MDS-based segmentation model for the fusion of contour and texture cues in natural images. Computer Vision and Image Understanding, 116(9), 981-990.
22 Zhuang, H., Low, K. S., & Yau, W. Y. (2012). Multichannel pulse-coupled-neural-network-based color image segmentation for object detection. Industrial Electronics, IEEE Transactions on, 59(8), 3299-3308.
23 Mignotte, M. (2011). Mds-based multiresolution nonlinear dimensionality reduction model for color image segmentation. Neural Networks, IEEE Transactions on, 22(3), 447-460.
24 Nath, S., Agarwal, S., & Kazmi, Q. A. (2011). Image histogram segmentation by multi-level thresholding using hill climbing algorithm. Int. J. Comput. Appl, 35(1).
25 Johari, H., Kaushik, V., & Upadhyay, P. K. (2010). Developing 3D Viewing Model from 2D Stereo Pair with its Occlusion Ratio. International Journal of Image Processing, 4(3), 251.
1 Google Scholar 
2 ScientificCommons 
3 Academic Index 
4 CiteSeerX 
5 refSeek 
6 iSEEK 
7 Socol@r  
8 ResearchGATE 
9 Bielefeld Academic Search Engine (BASE) 
10 OpenJ-Gate 
11 Scribd 
12 WorldCat 
13 SlideShare 
15 PdfSR 
16 Free-Books-Online 
A. C. Guyton, “A text book of medical Physiology”, W.B.Saunders company, Philadelphia, pp.784-824, (1976).
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.
Aghbari, Z. A., Al-Haj, R., “Hill-manipulation: An effective algorithm for color image segmentation”, Image Vision Comput. 24 (8), 894–903, 2006..
Cheng, H.D., Jiang, X.H., Sun, Y., Wang, J., “Color image segmentation: Advances and prospects”, Pattern Recognition 34,2259–2281, 2001.
Cheng, H.D., Li, J., “Fuzzy homogeneity and scale-space approach to color image segmentation”, Pattern Recognition 36, 1545–1562, 2003.
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
Gaurav Sharma, “Digital color imaging”, IEEE Transactions on Image Processing, Vol. 6, No.7, , pp.901-932, July1997.
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.
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.
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
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.
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.
M.Swain and D. Ballard, ”Color indexing”, International Journal of Computer Vision, Vol.7, no. 1,1991.
Milind M. mushrif, Ajoy K. Ray,”Color image segmentation:Rough-set theoretic approach” ,Elsevier Pattern Recognition Letters, pp 483-493,2008.
Pal, N.R., Pal, S.K., “A review on image segmentation techniques”, Pattern Recognition 26 (9), 1277–1294, 1993.
R. Unnikrishnan, M. Hebert, “Measures of Similarity”, IEEE Workshop on Computer Vision Applications, pp. 394–400, 2005.
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.
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.
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.
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.
Mr. Kishor K. Bhoyar
Yeshwantrao Chavan College of Engineering, Designation = Nagpur-10. - India
Dr. Omprakash G. Kakde
Vishweswarayya National Institute of Technology - India