Content Based Image Retrieval Using Radon Projections Approach

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 249)


CBIR systems are mainly used to retrieve images from huge database; the effectiveness of the CBIR system depends on the algorithm that is implemented for indexing. The way or method is used to determine similarities of available visual data by considering minute detailed low level features. Effectiveness of retrieval method depends on how the image is retrieved with maximum details and how much memory space is saved during retrieval process. Implementation of effective content-based image retrieval (CBIR) systems involves the combination of image creation, storage, security, transmission, analysis, evaluation feature extraction, and feature combination in order to store and retrieve images effectively. The goal of CBIR systems is to support image retrieval based on content i.e. shape, color, texture. In this paper we have implemented CBIR techniques using conventional Histogram and Radon Transform. Radon transform is based on projection of image intensity along a radial line oriented at a specific angle. We have test results on COREL1000 database. We have used Euclidean distance as a measure to calculate distance between two images and plot precision Vs Recall curve to show the effectiveness of the system.


Content based Image Retrieval Histogram Randon transform texture Pattern recognition system Euclidean distance Precision Recall 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Gupta, A., Jain, R.: Visual information retrieval. ACM Commun. 40(5), 70–79 (1997)CrossRefGoogle Scholar
  2. 2.
    Rui, Y., Huang, T.S., Change, S.F.: Image retrieval: current techniques, promising directions and open issues. J. Visual Commun. Image Representation 10(1), 39–62 (1999)CrossRefGoogle Scholar
  3. 3.
    Youssef, S.M.: ICTEDCT-CBIR: Integrating curvelet transform with enhanced dominant colors extraction and texture analysis for efficient content-based image retrieval. Elsevier, Computers and Electrical Engineering 38, 1358–1376 (2012)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image retrieval: Ideas, inuences & trends of the new age. ACM Computer Surv. 40(2), 160–173 (2008)Google Scholar
  5. 5.
    Safar, M., Shahabi, C., Sun, X.: Image retrieval by Shape: A comparative study. In: Proceedings of IEEE International Conference on Multimedia and Expo (ICME 2000), pp. 141–144 (2000)Google Scholar
  6. 6.
    Manjunath, B.S., et al.: Color and texture descriptors. IEEE Trans. CSVT 11(6), 703–715 (2001)Google Scholar
  7. 7.
    Sastry, C.S., Ravindranath, M., Pujari, A.K., Deekshatulu, B.L.: A modified Gabor function for content based image retrieval. Pattern Recognition Letters 28, 293–300 (2007)CrossRefGoogle Scholar
  8. 8.
    Bhagavathy, S., Chhabra, K.: A wavelet-based image retrieval system. -Technical report – ECE278A, Vision Research Laboratory, University of California (2007)Google Scholar
  9. 9.
    Suhasini, P.S., Sri Rama Krishna, K., Muralikrushna, I.V.: CBIR using color histogram processing. Journal of Theoretical and Applied Information Technology 6(1), 116–122 (2008)Google Scholar
  10. 10.
    Smith, J.R., Chang, S.-F.: Automated image retrieval using color and texture. Technical Report CU/CTR 408-95-14, Columbia University (1995)Google Scholar
  11. 11.
    Smith, J.R., Chang, S.-F.: Tools and techniques for color image retrieval. In: Symposium on Electronic Imaging: Science and Technology - Storage & Retrieval for Image and Video Databases IV, IS&T/SPIE, vol. 2670 (1996)Google Scholar
  12. 12.
    Natterer, F., Wubbeling, F.: Mathematical Methods in Image Reconstruction. SIAM (2001)Google Scholar
  13. 13.
    Averbuch, A., Shkolnisky, Y.: 3D Fourier based discrete Radon transform. Elsevier, Appl. Comput. Harmon. Anal. 15, 33–69 (2003)MathSciNetCrossRefMATHGoogle Scholar
  14. 14.
    Peter, T.: The Radon Transform-Theory and Implementation. PhD thesis, Dept. of Mathematical Modelling Section for Digital Signal Processing of Technical University of Denmark (1996)Google Scholar
  15. 15.
    Hoilund, C.: The Radon Transform. Aalborg University, VGIS (2007)Google Scholar
  16. 16.
    Asano, A.: Radon transformation and projection theorem. Topic 5, Lecture notes of Subject Pattern Information Processing (2002)Google Scholar
  17. 17.
    Averbuch, A., Coifman, R.R.: Fast Slant Stack: A notion of Radon Transform for Data in a Cartesian Grid which is Rapidly Computible, Algebraically Exact, Geometrically Faithful and Invertible. SIAM J. Scientific Computing (2001)Google Scholar
  18. 18.
    Kupce, E., Freeman, R.: The Radon Transform: A New Scheme for Fast Multidimensional NMR. Concepts in Magnetic Resonance, Wiley Periodicals 22, 4–11 (2004)CrossRefGoogle Scholar
  19. 19.
    Bracewell, R.N.: Two-Dimensional Imaging. Englewood Cliffs, pp. 505–537. Prentice Hall (1995)Google Scholar
  20. 20.
    Lim, J.S.: Two-Dimensional Signal and Image Processing. Englewood Cliffs, pp. 42–45. Prentice Hall (1990)Google Scholar
  21. 21.
    Muller, H., Muller, W., Squire, D.M., Maillet, S., Pun, T.: Performance evaluation in content based image retrieval: overview and proposals. Pattern Recognition Letters 22, 593–601 (2001)CrossRefGoogle Scholar
  22. 22.
    Manjunath, B.S., Ma, W.Y.: Texture features for browsing and retrieval of image data. IEEE Trans. Pattern Anal. Machine Intell. 18(8), 837–842 (1996)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.JSPM’s Bhivarabai Sawant Institute of Tech. & Research(W),WagholiJ.J.T. UniversityPuneIndia
  2. 2.Samsung Research and Development InstituteBangaloreIndia

Personalised recommendations