RGB Color Histogram Feature based Image Classification: An Application of Rough Reasoning

  • Shailendra Singh
Conference paper


In this paper, we have proposed a novel rough set based image classification method which uses RGB color histogram as features to classify images of different themes. We have used the concept of discernibility to analyze RGB color values and finding optimum color intervals to discern images of different themes. We have shown how the set of optimum color intervals for training set of images can be used to construct a representative sieve for training set of images to classify new set of images. We have also proposed rough membership based formulation to classify new set of images using representative sieve. The outlines of the system has been implemented and presented along with some results on a set of new images of different themes.


Training Image Image Classification Decision Table Decision Class Optimum Color 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chapelle, O., Haffner, P., Vapnik, V. N.: Support Vector machines for Histogram-Based Image Classification. IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 10(5). SEPTEMBER 1999Google Scholar
  2. 2.
    Dong, G.J., Zhang, Y.S., Fan, Y.H.: Remote Sensing Image Classification Algorithm Based on Rough Set Theory. Fuzzy Information and Engineering, Vol. (40). 846–851 (2007)CrossRefGoogle Scholar
  3. 3.
    Komorowski, J., Polkowski, L., Andrzej, S.: Rough Sets: A Tutorial. Scholar
  4. 4.
    Long, F., Zhang, H., and Feng, D.D.: Fundamentals of Content-based Image Retrieval, Microsoft Research Asia publication, available online at. Scholar
  5. 5.
    Mrowka, E., Dorado, A., Pedrycz, W., Izquierdo: Dimensionality Reduction for Content-Based Image Classification. Eighth International Conference on Information Visualisation, (IV’04) 435–438Google Scholar
  6. 6.
    Mohabey, A., Ray, A. K.: Rough set theory based segmentation of color images. Fuzzy Information Processing Society, 2000. NAFIPS. 19th International Conference of the North American, 338–342 (2000)Google Scholar
  7. 7.
    Park, S. B., Lee, J. W., Kim, S.K.: Content-based image classification using a neural network. Pattern Recognition Letters, Vol. 25(3). (2004) 287–300CrossRefGoogle Scholar
  8. 8.
    Pawlak, Z.: Rough Sets. Int. Journal of Computer and Information Sciences, Vol. 11(5). 341–356 (1982)MATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Sergyán, S.: Color Content-based Image Classification. 5th Slovakian-Hungarian Joint Symposium on Applied Machine Intelligence and Informatics, January 25–26, 2007Google Scholar
  10. 10.
    Shang, C., Shen, Q.: Aiding Neural Network Based Image Classification with Fuzzy-Rough Feature Selection. 2008 IEEE International Conference on Fuzzy Systems (FUZZ 2008)Google Scholar
  11. 11.
    Shih, J., L., Chen, L. W.: A Context-Based Approach for Color Image Retrieval, International Journal of Pattern Recognition and Artificial Intelligence, Vol. 16(2). 239–255. (2002)CrossRefGoogle Scholar
  12. 12.
    Singh, S., Dey, L.: A new Customized Document Categorization scheme using Rough Membership. International Journal of Applied Soft-Computing, Vol. 5(4), 373–390(Jul 2005)CrossRefGoogle Scholar
  13. 13.
    Vailaya, A., Figueiredo, M.A.T., Jain, A.K., Hong-Jiang Zhang: Image classification for content-based indexing. Image Processing, IEEE Transactions on Vol. 10(1). 117–130(Jan 2001)MATHCrossRefGoogle Scholar
  14. 14.
    Wang, S.L. Liew, A.W.C.: Information-Based Color Feature Representation for Image Classification. IEEE International Conference on Image Processing (ICIP 2007), Vol. 6. 353–356(Sept. 2007)Google Scholar
  15. 15.
    Yun. J., Zhanhuai, L., Yong W., Longbo Z.: A Better Classifier Based on Rough Set and Neural Network for Medical Images. Sixth IEEE International Conference on Data Mining — Workshops (ICDMW’06) 853–857(2006)Google Scholar

Copyright information

© Indian Institute of Information Technology, India 2009

Authors and Affiliations

  • Shailendra Singh
    • 1
  1. 1.Samsung India Software CenterNoidaIndia

Personalised recommendations