Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

This is an Open Access publication published under CSC-OpenAccess Policy.
Publications from CSC-OpenAccess Library are being accessed from over 74 countries worldwide.
Color Image Segmentation Technique Using “Natural Grouping” of Pixels
Nirmalya Chowdhury, Biplab Banerjee , Tanusree Bhattacharjee
Pages - 320 - 328     |    Revised - 30-08-2010     |    Published - 30-10-2010
Volume - 4   Issue - 4    |    Publication Date - October 2010  Table of Contents
Segmentation, Region Growing, Natural Grouping.
This paper focuses on the problem Image Segmentation which aims at sub dividing a given image into its constituent objects. Here an unsupervised method for color image segmentation is proposed where we first perform a Minimum Spanning Tree (MST) based “natural grouping” of the image pixels to find out the clusters of the pixels having RGB values within a certain range present in the image. Then the pixels nearest to the centers of those clusters are found out and marked as the seeds. They are then used for region growing based image segmentation purpose. After that a region merging based segmentation method having a suitable threshold is performed to eliminate the effect of over segmentation that may still persist after the region growing method. This proposed method is unsupervised as it does not require any prior information about the number of regions present in a given image. The experimental results show that the proposed method can find homogeneous regions present in a given image efficiently.
CITED BY (10)  
1 Jeyakumari, D. analysis on current trends in color image segmentation.
2 res Zaila, Y. L., Díaz-Romañach, M. L. B., & González-Hidalgo, M. (2014, June). A Graph for B h Based Segmentation Strategy Baggage Scanner Images. In Articulated Motion and Deformable Objects: 8th International Conference, AMDO 2014, Palma de Mallorca, Spain, July 16-18, 2014, Proceedings (Vol. 8563, p. 81). Springer.
3 Zaila, Y. L., Díaz-Romañach, M. L. B., & González-Hidalgo, M. (2014). A Graph Based Segmentation Strategy for Baggage Scanner Images. In Articulated Motion and Deformable Objects (pp. 81-93). Springer International Publishing.
4 Veer, S. M. Automatic Image Segmentation using Seeded Region Growing for Lung Tumor Detection.
5 Varghese, D., Majumdar, J., & Patil, M. B. S. (2014). Image Segmentation Algorithms.
6 Suji, G. E., Lakshmi, Y. V. S., & Jiji, G. W. (2013). Comparative Study on Image Segmentation Algorithms. International Journal of Advanced Computer Research, 3(3), 400-405.
7 Deb, D., & Roy, S. intelligent computing techniques on medical image segmentation and analysis: a survey.
8 Gupta, N. A. S. G. S. A Brief Study on MRI Image Segmentationwith its Applications and Techniques.
9 John, H., & Anitha, J. (2012). A Study of Image Segmentation Approaches. International Journal of Advanced Research in Electronics and Communication Engineering, 1(4), pp-62.
10 Madhusudhan, M., Malay, N., Nirmala, S. R., & Samerendra, D. (2011). Image Processing Techniques for Glaucoma Detection. Advances in Computing and Communications, 365-373.
1 Google Scholar 
2 Academic Index 
3 CiteSeerX 
4 iSEEK 
5 Socol@r  
6 Scribd 
7 SlideShare 
9 PdfSR 
1 H. Frigui, R. Krishnapuram. “Clustering by competitive agglomeration”. Pattern Recognition, 30(7):1109-1119, 1997
2 Y. Boykov. “Graph Cuts and Efficient N-D Image Segmentation”. International Journal of Computer Vision, 70(2):109–131, 2006
3 B. Sowmya, B. Sheelarani. “Colour Image Segmentation Using Soft Computing Techniques”. International Journal of Soft Computing Applications, 4:69-80, 2009
4 Li. Yanling and Yi. Shen. “Robust Image Segmentation Algorithm Using Fuzzy Clustering Based on Kernel-Induced Distance Measure”. In Proceedings of International Conference on Computer Science and Software Engineering, 2008
5 E. Dana, and P. F. Whelan. “Color image segmentation using a spatial k-means clustering algorithm”. In Proceedings of 10th International Machine Vision and Image Processing Conference, 2006
6 A. Borji, M. Hamidi and A. M. E. Moghadam. “CLPSO-based Fuzzy Color Image Segmentation”. In Proceedings of North American Fuzzy Information Processing Society, 2007
7 H. D. Cheng, C. H. Chen, H. H. Chiu, H. Xu. “Fuzzy Homogeneity Approach to Multilevel Thresholding”. IEEE Transaction On Image Processing, 7(7):1998
8 P. M. Birgani, M. Ashtiyani and S. Asadi. “MRI Segmentation Using Fuzzy C-means Clustering Algorithm Basis Neural Network”. In Proceedings of 3rd International Conference on Information and Communication Technologies: From Theory to Applications, 2008
9 J. F. David, K. Y. Yau, A. K. Elmagarmid. “Automatic Image Segmentation by Integrating Color-Edge Extraction and Seeded Region Growing”. IEEE Transactions On Image Processing, 10(10):2001
10 N. Chowdhury, C. A. Murthy: “Minimal spanning tree based clustering technique: Relationship with Bayes Classifier”. Pattern Recognition. 30(11): 1919-1929, 1997
11 A. Catherine A, Sugar, G. M. James (2003). "Finding the number of clusters in a data set: An information theoretic approach". Journal of the American Statistical Association. 98: 750–763, 2003
12 N.R. Pal, S.K. Pal. “A Review On Image Segmentation Techniques”. Pattern Recognition, 26(9): 1227-1294, 1993
13 R Adams, L. Bischof. “Seeded Region Growing”. IEEE Transaction on pattern analysis and machine intelligence, 16(6): 641-647, 1994
14 F. Y. Shih, S Cheng. “Automatic seeded region growing for color image segmentation”. Image And Vision Computing, 23:877-886, 2005
15 P. Jana and N. Chowdhury. “Finding the Natural Grouping in a Data Set Using MST of the Data Set”. In Proceedings of IICAI, 2005
16 H. D. Cheng, X. Jiang, Y. Sun, J. Wang. “Color image segmentation: advances and prospects”. Pattern Recognition, 34(12): 2259-2281, 2001
17 J. Harvey and Greenberg. “Greedy algorithms for minimum spanning tree”. University of Colorado, Denver, 1998
18 R. Nock, F. Nielsen. “Statistical Region Merging”. IEEE Transaction on Pattern Analysis. Machine Intelligence, 26(11): 1452-1458, 2004
19 T. W. Chen, Y. L. Chen and S. Y. Chien. “Fast image segmentation based on K-Means clustering with histograms in HSV color space”. In Proceedings of 10th IEEE Workshop on Multimedia Signal Processing, 2008
20 Image Segmentation Available at:http://www.en.wikipedia.com
Associate Professor Nirmalya Chowdhury
Jadavpur University - India
Mr. Biplab Banerjee
Jadavpur University - India
Miss Tanusree Bhattacharjee
Jadavpur University - India