Advertisement

Comparison of Content Based Image Retrieval System Using Wavelet Transform

Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 166)

Abstract

The large numbers of images has posed increasing challenges to computer systems to store and manage data effectively and efficiently. This paper implements a CBIR system using different feature of images through four different methods, two were based on analysis of color feature and other two were based on analysis of combined color and texture feature using wavelet coefficients of an image. To extract color feature from an image, one of the standard ways i.e. color histogram was used in YCbCr color space and HSV color space. Daubechies’ wavelet transformation and Symtels’ wavelet transform were performed to extract the texture feature of an image. After obtaining all experimental results, it has been inferred that wavelet based method gave a better performance as compared to color based method.

Keywords

CBIR wavelet transformation Color histogram YCbCr HSV 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Brodatz, P.: Textures: A Photographic Album for Artists and Designers, 128 p. Dover Publications (1966)Google Scholar
  2. 2.
    Stockman, G., Shapiro, L.: Computer Vision. Prentice Hall (2001)Google Scholar
  3. 3.
    Li, H.C., Wei, L., Guo, H.L.: Research and Implementation of an Image Retrieval Algorithm Based on Multiple Dominant Colors. J. Comput. Res. Dev. 36, 96–100 (1999)Google Scholar
  4. 4.
    Ali, A., Murtaza, S., Malik, A.S.: Content Based Image Retrieval Using Daubechies Wavelet Transform. In: Proceedings of the 2nd National Workshop on Trends in Information Technology, pp. 110–115 (2003)Google Scholar
  5. 5.
    Vadivel, A., Majumdar, A.K., Sural, S.: Characteristics of Weighted Feature Vector in Content-Based Image Retrieval Applications. In: Proceedings of International Conference on Intelligent Sensing and Information Processing (IEEE Cat. No.04EX783), pp. 127–132 (2004)Google Scholar
  6. 6.
    Vadivel, A., Majumdar, A.K., Sural, S.: Image Retrieval using Wavelet Based Texture Features. In: International Conference on Communications, Devices and Intelligent Systems, pp. 608–611 (2004)Google Scholar
  7. 7.
    Suhasini, P.S., Krishna, K.S.R., Krishna, V.M.: CBIR Using Color Histogram Processing. J. Theor. Appl. Inf. Technol. 6, 116–122 (2009)Google Scholar
  8. 8.
    Dubey, R., Choubey, R., Dubey, S.: Efficient Image Mining using Multi Feature Content Based Image Retrieval System. Int. J. Adv. Comput. Engg. Archit. 1, 17–25 (2011)Google Scholar
  9. 9.
    Khan, W., Kumar, S., Gupta, N., Khan, N.: A Proposed Method for Image Retrieval using Histogram values and Texture Descriptor Analysis. Int. J. Soft. Comput. Eng. 1, 33–36 (2011)Google Scholar
  10. 10.
    Khan, W., Kumar, S., Gupta, N., Khan, N.: Signature Based Approach For Image Retrieval Using Color Histogram and Wavelet Transform. Int. J. Soft. Comput. Engg. 1, 43–46 (2011)Google Scholar
  11. 11.
    Sharma, N., Rawat, P., Singh, J.: Efficient CBIR Using Color Histogram Processing. Signal and Image Processing: An. Int. J. 2, 94–112 (2011)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringBirla Institute of Technology, MesraRanchiIndia

Personalised recommendations