Comparison of SOM Algorithm and K-Means Clustering Algorithm in Image Segmentation

International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 46 - Number 22
Year of Publication: 2012
S. Ravikumar
A. Shanmugam

S Ravikumar and A Shanmugam. Article: Comparison of SOM Algorithm and K-Means Clustering Algorithm in Image Segmentation. International Journal of Computer Applications 46(22):21-25, May 2012. Full text available. BibTeX

	author = {S. Ravikumar and A. Shanmugam},
	title = {Article: Comparison of SOM Algorithm and K-Means Clustering Algorithm in Image Segmentation},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {46},
	number = {22},
	pages = {21-25},
	month = {May},
	note = {Full text available}


Image segmentation becomes simpler when the image is made up of smooth images. Many real world images are made up of a variety of smooth and textures regions, all of which need to identified in the segmentation algorithm. In such cases the existing methods fail to produce meaningful segmentation, successfully segmenting only the smooth or textured regions depending on the features used. The segmentation problem can be informally described as the task of partitioning an image into homogeneous regions. But in the textured images one of the main conceptual difficulties is the definition of a homogeneity measure in mathematical terms with of much complexity. By using a clustering algorithm, we can label the pixels of an image to form homogeneous functions or regions. Different clustering algorithms were commonly used in image segmentation algorithms. There are several issues related to image segmentation that require detailed review. The segmentation doesn't perform well if the grey levels of different objects are quite similar. This result in complex texture based image segmentation to use higher filter. But in future this technique used for dimensionality reduction to improve the speed.


  • T. Pavlidis, Algorithms for graphics and image processing, Springer, Berlin, 1982.
  • R. B. Ohlander, Analysis of natural scenes, PhD Thesis, Carnegie Institute of Technology, Dept. of Computer Science, Carnegie-Mellon University, Pittsburgh, PA, 1975
  • M. Cheriet, J. N. Said and C. Y. Suen, A recursive thresholding technique for image segmentation, IEEE Transactions on Image Processing, 1998.
  • N. Otsu, A threshold selection method from grey level histograms, IEEE Transactions on Systems, Man and Cybernetics,1978.
  • L. Li, J. Gong and W. Chen, Gray-level image thresholding based on Fisher linear projection of two-dimensional histogram, Pattern Recognition, 1997.
  • N. Ahuja, A. Rosenfeld and R. M. Haralick, Neighbour gray levels as features in pixel classification, Pattern Recognition, 1980.
  • J. M. Prager, Extracting and labeling boundary segments in natural scenes, IEEE Transactions on Pattern Analysis and Machine Intelligence, 980.
  • W. A. Perkins, Area segmentation of images using edge points, IEEE Transactions on Pattern Recognition and Machine Intelligence, 1980.
  • F. H. Y. Chan, F. K. Lam and H. Zhu, Adaptive thresholding by variational method, IEEE Transactions on Image Processing, 1998.
  • K. Cho and P. Meer, Image segmentation from consensus information, Computer Vision and Image Understanding, 1997.
  • M. Yeung, B. L. Yeo and B. Liu, Segmentation of video by clustering and graph analysis, Computer Vision and Image Processing, 1998.
  • Y. L. Chang and X. Li, Adaptive image region growing, IEEE Transactions on Image Processing,, 1994.
  • R. Adams and L. Bischof, Seeded region growing, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1994.
  • Mehnert and P. Jackway, An improved seeded region growing algorithm, Pattern Recognition Letters, 1997.
  • S. Basu, Image segmentation by semantic method, Pattern Recognition, 1987.
  • J. P. Gambotto, A new approach to combining region growing and edge detection, Pattern Recognition Letters, 1993.
  • S. A. Hojjatoleslami and J. Kittler, Region growing: a new approach, CVSSP Technical Report TR-6/95, University of Surrey, Department of Electronic and Electrical Engineering, 1995.
  • S. W. Lu and H. Xu, Textured image segmentation using autoregressive model and artificial neural network, Pattern Recognition, 1995.
  • H. He and Y. Q. Chen, Unsupervised texture segmentation using resonance algorithm for natural scenes, Pattern Recognition Letters, 2000.
  • S. Singh, R. Al-Mansoori, Identification of regions of interest in digital mammograms, Journal of Intelligent Systems, 2000.