Skip to main content

Advertisement

SpringerLink
Log in

Color classification using margin-setting with ellipsoids

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

This paper presents a robust and computationally efficient genetic algorithm for color classification. It designs well-fitted color space prolate spheroids (ellipsoids) that envelop the training pixels. The ellipsoids are then used to classify unlabeled image pixels in accordance with their color, in order to partition the image. The color classification algorithm described here has very low error rates, boasts very high operational speed, and permits trading higher indecision rates for lower rates of misclassification. The performance of the color classifier developed in this paper is compared with those of the support vector machine (SVM) and the nearest-neighbor (kNN) classifiers. It has been shown that our color classifier outperforms SVM and kNN for partitioning of color images that contain several closely spaced color classes. It has higher correct classification, lower misclassification, and significantly reduced operational latency in comparison with color classifiers based on kNN and SVM.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Pal N.R., Pal S.K.: A review of image segmentation techniques. Pattern Recognit. 26(9), 1277–1294 (1999)

    Article  Google Scholar 

  2. Carson C., Belongie S., Greenspan H., Malik J.: Blobworld: image segmentation using expectation-maximization and its application to image querying. In: IEEE Trans. Pattern Anal. Mach. Intell. 24(8), 1026–1038 (2002)

    Google Scholar 

  3. Cheng H.D., Jiang X.H., Sun Y., Wang J.: Color image segmentation: advances and prospects. Pattern Recognit. 34(12), 2259–2281 (2001)

    Article  MATH  Google Scholar 

  4. Meyer, F.: Color image segmentation. In: Proceedings of the IEE International Conference Image Processing and its Applications, pp. 303–306 (1992)

  5. Liu J., Yang Y.H.: Multiresolution color image segmentation. In: IEEE Trans. Pattern Recognit. Mach. Intell. 16(7), 689–700 (1994)

    Google Scholar 

  6. Boykov Y., Funka-Lea G.: Graph cuts and efficient N-D image segmentation. Int. J. Comput. Vis. 70(2), 109–131 (2006)

    Article  Google Scholar 

  7. Choi H., Baraniuk R.G.: Multiscale image segmentation using wavelet-domain hidden Markov models. In: IEEE Trans. Image Process. 10(9), 1309–1321 (2001)

    MathSciNet  Google Scholar 

  8. Fan J., Yau D.K.Y., Elmagarmid A.K., Aref W.G.: Automatic image segmentation by integrating color-edge extraction and seeded region growing. In: IEEE Trans. Image Process. 10(10), 1454–1466 (2001)

    MATH  Google Scholar 

  9. Vandenbroucke N., Macaire L., Postaire J.G.: Color image segmentation by pixel classification in an adapted hybrid color space. Application to soccer image analysis. Comput. Vis. Image Underst. 90(2), 190–216 (2003)

    Article  Google Scholar 

  10. Asano, T., Chen, D.Z., Katoh, N., Tokuyama, T.: Polynomial-time solutions to image segmentation. In: Proceedings of the 7th Annual SIAM-ACM Conference on Discrete Algorithms, pp. 104–113 (1996)

  11. Beveridge J.R., Griffith J., Kohler R.R., Hanson A.R., Riseman E.M.: Segmenting images using localized histograms and region merging. Int. J. Comput. Vis. 2, 311–347 (1989)

    Article  Google Scholar 

  12. Lucchese, L., Mitra, S.K.: Unsupervised low frequency driven segmentation of color images. In: Proceedings of the IEEE International Conference on Image Processing, vol. 3, pp. 240–244. Kobe (1999)

  13. Delignon Y., Marzouki A., Pieczynski W.: Estimation of generalized mixtures and its application in image segmentation. In: IEEE Trans. Image Process. 6(10), 1364–1375 (1997)

    Google Scholar 

  14. Gao H., Siu W.C., Hou C.H.: Improved techniques for automatic image segmentation. In: IEEE Trans. Circuits Syst. Video Technol. 11(12), 1273–1280 (2001)

    Google Scholar 

  15. Athanasiadis T., Mylonas P., Avrithis Y., Kollias S.: Semantic image segmentation and object labeling. In: IEEE Trans. Circuits Syst. Video Technol. 17(3), 298–312 (2007)

    Google Scholar 

  16. Prado, A.: Semantic image segmentation using morphological tools. In: Proceedings of the IEEE International Conference on Image Processing, vol. 2, pp. 745–748 (2002)

  17. Carneiro, G., Vasconcelos, N.: Formulating semantic image annotation as a supervised learning problem. In: Proceedings of CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 163–168 (2005)

  18. Tremeau A., Colantoni P.: Regions adjacency graph applied to color image segmentation. In: IEEE Trans. Image Process. 9, 735–744 (2000)

    Google Scholar 

  19. Caulfield H.J., Heidary K.: Exploring margin setting for good generalization in multiple class discrimination. Pattern Recognit. 38, 1225–1238 (2005)

    Article  MATH  Google Scholar 

  20. Heidary K., Caulfield H.J.: Discrimination among similar looking, noisy color patches using Margin Setting. Opt. Express J. Opt. Soc. Am. 15(1), 62–75 (2007)

    Article  Google Scholar 

  21. Heidary K., Caulfield H.J.: Margin-setting with hyperellipsoidal surfaces. Opt. Mem. Neural Netw. (Inf. Opt.) 19(2), 97–109 (2010)

    Article  Google Scholar 

  22. Heidary K., Caulfield H.J.: Application of supergeneralized matched filters to target classification. Appl. Opt. 44(1), 47–54 (2005)

    Article  Google Scholar 

  23. Heidary, K., Caulfield, H.J.: Needles in a haystack: fast spatial search for targets in similar-looking backgrounds. J. Franklin Inst. (2012, accepted)

  24. Caulfield H.J.: Artificial color. Neurocomputing 51, 463–465 (2003)

    Article  Google Scholar 

  25. Caulfield H.J.: Nature’s alternative to hyperspectral imaging and why nature is right. SPIE 4787, 132–136 (2002)

    Google Scholar 

  26. Caulfield H.J., Fu J., Yoo S.M.: Artificial color imaging logic. Inf. Sci. 167, 1–7 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  27. Vapnik V.: The Nature of Statistical Learning Theory. Springer, New York (1995)

    Book  MATH  Google Scholar 

  28. Vapnik V.: Statistical Learning Theory. Wiley, New York (1998)

    MATH  Google Scholar 

  29. Burgess C.J.C.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2(2), 955–974 (1998)

    Google Scholar 

  30. Shawe-Taylor J., Cristianini N.: Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, Cambridge (2000)

    Google Scholar 

  31. Kibriya, A.M., Franl, E.: An empirical comparison of exact nearest neighbor algorithms. In: Proceedings of the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD ’07), Lecture Notes in Computer Science, vol. 4702, pp. 140–151. Springer, Berlin

  32. Wu, X., Holmes, G., Pfahringer, B.: Mining arbitrarily large databases using heuristic k-nearest neighbour searc. In: AI 2008: Advances in Artificial Intelligence, Lecture Notes in Computer Science, vol. 5360, pp. 355–361. Springer, Berlin

  33. Koljonen J.: Comparison of nearest point algorithms by genetic algorithms. Expert Syst. Appl. 38(8), 10303–10311 (2011)

    Article  Google Scholar 

  34. http://www.vision.caltech.edu/Image_Datasets/Caltech101/Caltech101.html

  35. Gonzalez R.C., Woods R.E.: Digital Image Processing. Prentice Hall, New York (2002)

    Google Scholar 

  36. Dougherty, N. (ed.): Mathematical Morphology in Image Processing. CRC Press, Boca Raton (1992)

    Google Scholar 

  37. Shih F.Y.: Image Processing and Mathematical Morphology Fundamentals and Applications. CRC Press, Boca Raton (2009)

    Book  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Alabama A&M University, PO Box 702, Normal, AL, 35762, USA

    Kaveh Heidary

  2. Research Institute, Alabama A&M University, PO Box 313, Normal, AL, 35762, USA

    H. John Caulfield

Authors
  1. Kaveh Heidary
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. H. John Caulfield
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kaveh Heidary.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Heidary, K., Caulfield, H.J. Color classification using margin-setting with ellipsoids. SIViP 8, 1245–1262 (2014). https://doi.org/10.1007/s11760-012-0349-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-012-0349-6

Keywords

Search

Navigation