Advertisement

A Novel Method for CBIR Using Texture Spectrum in Wavelet Domain

  • G. Rosline Nesa Kumari
  • M. Sudheer
  • R. Tamilkodi
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 248)

Abstract

This paper presents an effective color image retrieval scheme by contrast based texture features, which achieves higher retrieval efficiency. The proposed method Texture Spectrum in Wavelet (TSW) domain is very fast to compute and enable to speed up the wavelet computation phase for thousands of sliding windows of varying sizes in an image. Proposed TSW provides a robust contrast features for image retrieval from a lot of objects in an image can be distinguished solely by their textures without any other information. The proposed system divides the lower resolution approximation image into four sections, where each section extracts 12 contrast features and stores the features of the query image and also all images in the database and extracts the features of each image. The proposed TSW method reduces the computation of possible patterns as well as fast and retrieving accurate images. Experimental results show that the proposed method for image retrieval is more accurate, efficient and quite understandable in spite of the availability of the existing retrieving algorithms.

Keywords

Image retrieval Texture Robust Feature Query image DWT 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Lew, M.S., Sebe, N., Djeraba, C., Jain, R.: Content-based multimedia information retrieval: State of the art and challenges. ACM Trans. Multimedia Comput. Commun. Appl. 2(1), 1–19 (2006)CrossRefGoogle Scholar
  2. 2.
    Kato, T.: Database architecture for content-based image retrieval. In: Proceedings of the SPIE - The International Society for Optical Engineering, vol. 1662, pp. 112–113 (1992)Google Scholar
  3. 3.
    Chang, S.-K., Yan, C.W., Dimitroff, D.C., Arndt, T.: An intelligent image database system. IEEE Trans. Software Eng. 14(5) (1988)Google Scholar
  4. 4.
    Narasimhalu, A.D.: Special section on content-based retrieval. Multimedia Systems (1995)Google Scholar
  5. 5.
    Carson, I.C., Belongie, S., Greenspan, H., Malik, J.: Blobworld: image segmentation using expectation-maximization and its application to image querying. IEEE Trans. Pattern Anal. Mach. Intell. 8(8), 1026–1038 (2002)CrossRefGoogle Scholar
  6. 6.
    Chang, S.-K., Hsu, A.: Image information systems: Where do we go from here? IEEE Trans. on Knowledge and Data Engineering 4(5) (1992)Google Scholar
  7. 7.
    Gudivada, V.N., Raghavan, J.V.: Special issue on content-based image retrieval systems. IEEE Computer Magazine 28(9) (1995)Google Scholar
  8. 8.
    Tamura, H., Yokoya, N.: Image datab ase systems: A survey. Pattern Recognition 17(1) (1984)Google Scholar
  9. 9.
    Wang, J., Wiederhold, G., Firschein, O., We, S.: Content-based Image Indexing and Searching Using Daubechies’ Wavelets. International Journal on Digital Libraries (IJODL) 1(4), 311–328 (1998)CrossRefGoogle Scholar
  10. 10.
    Natsev, A., Rastogi, R., Shim, K.: WALRUS: A Similarity Retrieval Algorithm for Image Databases. In: Proceeding. ACM SIGMOD Int. Conf. Management of Data, pp. 395–406 (1999)Google Scholar
  11. 11.
    Ardizzoni, S., Bartolini, I., Patella, M.: Windsurf: Region based Image Retrieval using Wavelets. In: IWOSS 1999, pp. 167–173 (1999)Google Scholar
  12. 12.
    Wouwer, G.V.D., Scheunders, P., Dyck, D.V.: Statistical texture characterization from discrete wavelet representation. IEEE Transactions on Image Processing 8, 592–598 (1999)CrossRefGoogle Scholar
  13. 13.
    Livens, S., Scheunders, P., Wouwer, G.V.D., Dyck, D.V.: Wavelets for texture analysis, an overview. In: Proceedings of Sixth International Conference on Image Processing and Its Applications, vol. 2, pp. 581–585 (1997)Google Scholar
  14. 14.
    Gudivada, V.N., Raghavan, J.V.: Special issue on content based image retrieval systems. IEEE Computer Magazine 28(9) (1995)Google Scholar
  15. 15.
    Pentland, A., Picared, R.: Special issue on digital libraries. IEEE Trans. Patt. Recog. and Mach. Intell. (1996)Google Scholar
  16. 16.
    Narasimhalu, A.D.: Special issue on content based retrieval. Multimedia Systems (1995)Google Scholar
  17. 17.
    Jain, R. (Guest ed.): Special issue on Visual information management. Comm. ACM (December 1997)Google Scholar
  18. 18.
    Schatz, B., Chen, H.: Buiding large scale digital libraries. Computer (1996)Google Scholar
  19. 19.
    Vogel, J., Schiele, B.: Performance evaluation and optimization for content-based image retrieval. Pattern Recognition 39(5), 897–909 (2006)CrossRefMATHGoogle Scholar
  20. 20.
    Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital Image Processing Using MALAB. Pearson Education (2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • G. Rosline Nesa Kumari
    • 1
  • M. Sudheer
    • 1
  • R. Tamilkodi
    • 1
  1. 1.Godavari Institute of Engineering & TechnologyRajahmundryIndia

Personalised recommendations