Communications - Scientific Letters of the University of Zilina 2012, 14(3):68-72 | DOI: 10.26552/com.C.2012.3.68-72

The Effect of Metric Space on the Results of Graph Based Colour Image Segmentation

Martina Zachariasova1, Robert Hudec1, Miroslav Benco1, Patrik Kamencay1, Peter Lukac1, Slavomir Matuska1
1 Department of Telecommunications, University of Zilina, Slovakia

This paper deals with the impact of the metric space on the results of colour image segmentation algorithm. Distance and similarity measures are important tasks for quality of colour image segmentation. Main idea of this research is to make a comparison of algorithm results with using different metrics. Euclidean distance is the most used metric in many colour image segmentation algorithms. This paper shows comparison of this metric with many other metrics. Nine different metrics are gradually used in efficient graph based colour image segmentation algorithm created in the C++ language. The efficiency of precision and recall is one of the investigation tasks of colour image segmentation.

Keywords: image, segmentation, metric, distance, similarity

Published: September 30, 2012  Show citation

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Zachariasova, M., Hudec, R., Benco, M., Kamencay, P., Lukac, P., & Matuska, S. (2012). The Effect of Metric Space on the Results of Graph Based Colour Image Segmentation. Communications - Scientific Letters of the University of Zilina14(3), 68-72. doi: 10.26552/com.C.2012.3.68-72
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References

  1. MORAVCIK, T.: Image Segmentation in Programming Environment MATLAB, 11 International PhD Workshop OWD 2009, Wisla, pp. 469-470, October 2009.
  2. ALSULTANNY, Y. A.: Color Image Segmentation to the RGB and HSI Model Based on Region Growing Algorithm, Proc. of the 4th WSEAS intern. conference on Computer Engineering and Applications, Cambridge, pp. 63-68, USA, 2010.
  3. HEDJAM, R., MIGNOTTE, M.: A Hierarchical Graph-Based Markovian Clustering Approach for the Unsupervised Segmentation of Textured Color Images, Proc. of IEEE Int. Conf. Image Process., Cairo, Egypt, pp. 1365-1368, 2009. Go to original source...
  4. SHI, J., MALIK, J.: Normalized Cuts and Image Segmentation, IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 8, pp. 888-905, 2000. Go to original source...
  5. DONG, G., XIE, M.: Color Clustering and Learning for Image Segmentation Based on Neural Networks, IEEE Trans. Neural Netw., vol. 16, no. 4, pp. 925-936, 2005. Go to original source...
  6. COMANICIU, D., MEER, P.: Mean Shift: A Robust Approach Toward Feature Space Analysis, IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 5, pp. 603-619, 2002. Go to original source...
  7. MIGNOTTE, M.: Segmentation by Fusion of Histogram-Based K-Means Clusters in Different Color Spaces, IEEE Trans. Image Process., vol. 17, no. 5, pp. 780-787, 2008. Go to original source...
  8. BHOYAR, K., KAKDE, O.: Color Image Segmentation based on JND Color Histogram, Int. J. of Image Process., vol. 3, no. 6, pp. 283-292, 2010.
  9. DENG, Y., MANJUNATH, B. S.: Unsupervised Segmentation of Color-texture Regions in Images and Video, IEEE Trans. Pattern Anal. Mach. Intell, vol. 23, no. 8, pp. 1-26, 2001. Go to original source...
  10. CLAIRET, J., BIGAND, A., COLOT, O.: Color Image Segmentation using Type-2 Fuzzy Sets, IEEE Inter. Conf. on E-Learning in Indust. Elec., pp. 52-57, 2006. Go to original source...
  11. FELENZWALB, P. F.. HUTTENLOCHER, D. P: Efficient Graph-based Image Segmentation, Int. J. of Computer Vision, vol. 59, No. 2, pp. 1-26, 2004. Go to original source...
  12. MACHAJ, J., BRIDA, P.: A Comparison of Similarity Measurements for Database Correlation based Indoor Positioning System, 2011. Go to original source...
  13. CHA, S. H.: Comprehensive Survey on Distance/Similarity Measures between Probability Density Functions, Int. J. of Mathematical Models and Methods in Applied Science, vol. 1, no. 4, pp. 300-307, 2007.
  14. KRAUSE, E. F.: Taxicab Geometry an Adventure in Non-Euclidean Geometry, Dover Publications, pp. 96, 1986.
  15. TAX, D. M. J., DUIN, R., RIDDER, D. D.: Classification, Parameter Estimation and State Estimation: an Engineering Approach using Matlab, John Wiley and Sons, pp. 419, 2004.
  16. WANG, J. Z.: Berkeley 1000 image database. [online 10.06.2011] Available on the internet. http://wang.ist.psu.edu/~jwang/test1.tar.
  17. LUKAC, P., HUDEC, R., BENCO, M., DUBCOVA, Z., ZACHARIASOVA, M., KAMENCAY, P.: The Evaluation Criterion for Color Image Segmentation Algorithms, J. of Electrical Engineering (JEEEC), vol. 63, no. 1, pp. 13-20, 2012. Go to original source...

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