Advertisement

Combining Wavelet Saliency, Color and DCT Coefficients for Content-Based Image Retrieval

  • Alberto Rios Júnior
  • Díbio Leandro Borges
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8827)

Abstract

This paper presents an approach for content-based image retrieval extracting salient points and regions from images, and also aggregating color and DCT values in a signature descriptor for recognition. Salient points and regions are extracted from each image by a wavelet decomposition over the color channels where the highest coefficients in coarsest levels are the centers of salient regions in finest resolution. These local regions are support for extracting color histograms and a set of DCT magnitudes in order to derive a signature for the image. A feature vector combining histograms of color channels and DCT values is proposed and tested as signature of the image. Public COIL, Caltech, and ZuBuD images datasets are used for testing. Results comparing variations of the descriptor based on wavelet saliency are given on all those image datasets supporting potential for the proposed method.

Keywords

content-based image retrieval wavelet salient points global signature category recognition 

References

  1. 1.
    Bimbo, A.D.: Visual Information Retrieval. Morgan Kaufmann (1999)Google Scholar
  2. 2.
    Caltech: Caltech Categories Site (2014), http://www.vision.caltech.edu/html-files/archive.html (accessed May 4, 2014)
  3. 3.
    Geusebroek, J., Van den Boomgaard, R., Smeulders, A., Geerts, H.: Color invariance. IEEE Trans. on PAMI 23(12), 1338–1350 (2001)CrossRefGoogle Scholar
  4. 4.
    Laurent, C., Laurent, N., Maurizot, M., Dorval, T.: In depth analysis and evaluation of saliency-based color image indexing methods using wavelet salient features. Multimedia Tools and Applications 31(1), 73–94 (2006)CrossRefGoogle Scholar
  5. 5.
    Liu, Y., Zhang, D., Lu, G., Ma, W.Y.: A survey of content-based image retrieval with high-level semantics. Pattern Recognition 40(1), 262–282 (2007)CrossRefzbMATHGoogle Scholar
  6. 6.
    Loupias, E., Sebe, N., Brest, S., Jolion, J.: Wavelet-based salient points for image retrieval. In: ICIP 2000, vol. 2, pp. 518–521. IEEE (2000)Google Scholar
  7. 7.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  8. 8.
    Mallat, S.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. on PAMI 11(7), 674–693 (1989)CrossRefzbMATHGoogle Scholar
  9. 9.
    Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. on PAMI 27(10), 1615–1630 (2005)CrossRefGoogle Scholar
  10. 10.
    Nayar, S., Nene, S., Murase, H.: Columbia object image library (coil 100). Tech. rep., Dept Computer Science, Columbia University, USA (1996)Google Scholar
  11. 11.
    Sebe, N., Lew, M.: Comparing salient point detectors. Pattern Recognition Letters 24(1), 89–96 (2003)CrossRefzbMATHGoogle Scholar
  12. 12.
    Shao, H., Svoboda, T., Van Gool, L.: Zubud: zurich buildings database for image based recognition. Tech. rep., Computer Vision Lab, Swiss Federal Institute of Technology, Switzerland (2003)Google Scholar
  13. 13.
    Tian, Q., Sebe, N., Loupias, E., Huang, T., Lew, M.: Image retrieval using wavelet-based salient points. Journal of Electronic Imaging 10(4), 835–849 (2001)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Alberto Rios Júnior
    • 1
  • Díbio Leandro Borges
    • 2
  1. 1.Department of Mechanical EngineeringUniversity of BrasíliaBrasíliaBrazil
  2. 2.Department of Computer ScienceUniversity of BrasíliaBrasíliaBrazil

Personalised recommendations