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Image Segmentation for Different Color Spaces using Dynamic Histogram based Rough-Fuzzy Clustering Algorithm

by E. Venkateswara Reddy, E. S. Reddy
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 85 - Number 14
Year of Publication: 2014
Authors: E. Venkateswara Reddy, E. S. Reddy
10.5120/14912-3516

E. Venkateswara Reddy, E. S. Reddy . Image Segmentation for Different Color Spaces using Dynamic Histogram based Rough-Fuzzy Clustering Algorithm. International Journal of Computer Applications. 85, 14 ( January 2014), 35-40. DOI=10.5120/14912-3516

@article{ 10.5120/14912-3516,
author = { E. Venkateswara Reddy, E. S. Reddy },
title = { Image Segmentation for Different Color Spaces using Dynamic Histogram based Rough-Fuzzy Clustering Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { January 2014 },
volume = { 85 },
number = { 14 },
month = { January },
year = { 2014 },
issn = { 0975-8887 },
pages = { 35-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume85/number14/14912-3516/ },
doi = { 10.5120/14912-3516 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:02:29.073673+05:30
%A E. Venkateswara Reddy
%A E. S. Reddy
%T Image Segmentation for Different Color Spaces using Dynamic Histogram based Rough-Fuzzy Clustering Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 85
%N 14
%P 35-40
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper describes a comparative study of color image segmentation for various color spaces such as RGB, YUV, XYZ, Lab, HSV, YCC and CMYK using Dynamic Histogram based Rough Fuzzy C Means (DHRFCM). The proposed algorithm DHRFCM is based on modified Rough Fuzzy C Means (RFCM), which is further divided into three stages. In the pre-processing stage, convert RGB into required color space and then select the initial seed points by constructing histogram. In the next phase, use the rough sets to reduce the seed point selection and then apply Fuzzy C-Means algorithm to segment the given color image. The proposed algorithm DHRFCM produces an efficient segmentation for color images when compared with RFCM and also the unsupervised DHRFCM algorithm is compared with different clustering validity indices such as Davies-Bouldin (DB index), Rand index, silhouette index and Jaccard index and their computational time for various color spaces. Experimental results shows that the proposed method perform well and improve the segmentation results in the vague areas of the image.

References
  1. Skowron A. , Stepaniuk,J. : Information granules in distributed environment. In: Zhong, N. , Skowron, A. , Ohsuga,S. . (eds. ) RSFDGr C 1999. LNCS (LNAI) Vol. 1711, pp. 357–365. Springer, Heidelberg (1999).
  2. Wei Zhang, Yu-zhu Zhang, Cheng Li, "A new hybrid algorithm for image segmentation based on rough sets and enhanced fuzzy c-means clustering", IEEE, International Conference on Automation and Logistics, August 2009.
  3. Milinda M. Mushrif, Ajoy K. Ray, "Color image segmentation: Rough-set theoretic approach", Pattern Recognition Letters, Elsevier, 2008
  4. S Mohapatra, D Patra, "Unsupervised Leukocyte Image Segmentation Using Rough Fuzzy Clustering", ISRN Artificial Intelligence, 2012
  5. Hassanien, A. E. , A. Abraham, J. F. Peters, G. Schaefer, and C. Henry. 2009. Rough sets and near sets in medical imaging: A review. IEEE Transactions on Information Technology in Biomedicine 13(6):955–968. Digital object identifier: 10. 1109/TITB. 2009. 2017017.
  6. Beaubouef, Theresa, Frederick E Petry, and Gurdial Arora. 1998. Information-theoretic measures of uncertainity for rough sets and rough relational databases. Information Sciences 109(1-4):185–195.
  7. Martin, D. ,C. Fowlkes, D. Tal, and J. Malik. 2001. A database of human segmented natural images and its application to evaluating segmentation algorithms and measing ecological statistics. In proceedings of the 8th international conference on computer vision, vol. 2, 416–423.
  8. Bhatt, R B, and M Gopal. 2004. Frid: fuzzy-rough interactive dichotomizers. In Proceedings of the ieee international conference on fuzzy systems, 1337–1342.
  9. N. Mac Parthalain, R. Jensen, and Q. Shen. Comparing Fuzzy -Rough and Fuzzy Entropy-assisted Fuzzy-Rough Feature Selection. Proceedings of the 6th Annual Workshop on Computational Intelligence (UKCI'06), 2006.
  10. Z. Bao, B. Han, and S. Wu. A novel clustering algorithm based on variable precision rough-fuzzy sets, Proceedings of the International conference on in- telligent computing, ICIC 2006, Kunming, China, August 16-19, pp. 284-289, 2006.
  11. S. Asharaf and M. N. Murty. An adaptive rough fuzzy single pass algorithm for clustering large data sets, Pattern Recognition, vol. 36, no. 12, pp. 3015–3018, 2004.
  12. W. H. Au and K. C. C. Chan. An Effective Algorithm for Discovering Fuzzy Rules in Relational Databases. Proceedings of the 7th IEEE International Conference on Fuzzy Systems, pp. 1314–1319, 1998.
  13. Banerjee, M. , Mitra, S. , Pal, S. K. : Rough-Fuzzy MLP: Knowledge Encoding and Classification.
  14. S Mohapatra, D Patra, "Unsupervised Leukocyte Image Segmentation Using Rough Fuzzy Clustering", ISRN Artificial Intelligence, 2012
  15. Jensen, R. , Shen, Q. : Fuzzy-Rough Attribute Reduction with Application to Web Categorization, Fuzzy Sets and Systems, 141, 2004, 469–485.
  16. Krishnapuram, R. , Keller, J. M. : A Possibilistic Approach to Clustering, IEEE Transactions on Fuzzy Systems, 1(2), 1993, 98–110.
  17. E. V. Reddy, Dr. E. S. Reddy "Image Segmentation using Rough set based Fuzzy K Means clustering algorithm", Global Journals Inc, GJCST Volume 13 Issue-6 Version 1. 0 ISSN : Online: 0975-4172 Print: 0975-4350
  18. E. V. Reddy, G. V. Suresh, Dr. E. S. Reddy "rough set analysis for uncertain data classification", IEEE Explore, 2010.
  19. E. V. Reddy, G. V. Suresh "uncertain data classification using rough set theory" advances in intelligent and soft computing series: 4240 of Springer.
Index Terms

Computer Science
Information Sciences

Keywords

Rough Sets Dynamic Histogram Fuzzy C-Means algorithm Rough Fuzzy C-Means algorithm histogram RGB YUV HSV XYZ LAB CMYK conversions

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