Optimal Matching of Images Using Combined Color Feature and Spatial Feature

  • Xin Huang
  • Shijia Zhang
  • Guoping Wang
  • Heng Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3991)


In this paper we develop a new image retrieval method based on combined color feature and spatial feature. We introduce an ant colony clustering algorithm, which helps us develop a perceptually dominant color descriptor. The similarity between any two images is measured by combining the dominant color feature with its spatial feature. The optimal matching theory is employed to search the optimal matching pair of dominant color sets of any two images, and the similarity between the query image and the target image is computed by summing up all the distances of every matched pair of dominant colors. The algorithm introduced in this paper is well suited for creating small spatial color descriptors and is efficient. It is also suitable for image representation, matching and retrieval.


Spatial Feature Color Feature Optimal Match Dominant Color CIELAB Color Space 
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.


  1. 1.
    Smith, J.R., Chang, S.F.: A fully automated content-based image query system. In: Proceedings of ACM Multimedia, pp. 87–98 (1996)Google Scholar
  2. 2.
    Zhang, L., Lin, F., Zhang, B.: A CBIR method based on color-spatial feature. In: IEEE Regin10 Annual International Conference, pp. 166–169 (1999)Google Scholar
  3. 3.
    Ma, W.Y., Deng, Y., Manjunath, B.S.: Tools for texture/color base search of images. In: Proc. SPIE, vol. 3016, pp. 496–505 (1997)Google Scholar
  4. 4.
    Mojsilovic, A., Hu, J., Soljanin, E.: Extraction of perceptually important colors and similarity measurement for image matching, retrieval, and analysis. IEEE Trans. Image Processing 11, 1238–1248 (2002)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Mojsilovic, A., Kovacevic, J., Hu, J., et al.: Matching and retrieval based on the vocabulary and grammar of color patterns. IEEE Trans. Image Processing 9, 38–54 (2000)CrossRefGoogle Scholar
  6. 6.
    Rogowitz, B., Frese, T., Smith, J., Bouman, C.A., Kalin, E.: Perceptual image similarity experiments. In: Proc. SPIE (1997)Google Scholar
  7. 7.
    Deneubourg, J.L., Goss, S., Frank, N.: The dynamics of collective sorting: robot-like ants and ant-like robots. In: Proc. Of the 1st Int. Conference on Simulation of Adaptive Behavior: From Animals to Animats, pp. 356–363. MIT Press/ Bradford Books, Cambridge (1991)Google Scholar
  8. 8.
    Lumer, E., Faieta, B.: Diversity and adaption in populations of clustering ants. In: Proc. of the 3rd International conference on Simulation of Adaptive Behaviour: From Animals to Animats, vol. 3, pp. 501–508. MIT Press, Cambridge (1994)Google Scholar
  9. 9.
    Parpinelli, R.S., Lopes, H.S., Freitas, A.A.: Data mining with an ant colony opti-mization algorithm. IEEE Trans. on Evolutionary Computing 6, 321–332 (2002)CrossRefGoogle Scholar
  10. 10.
    Yap, P.T., Paramesran, R.: Seng-Huat Ong, Image analysis by Krawtchouk moments. IEEE Transactions on Image Processing 12, 1367–1377 (2003)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Kenneth, R.: Castleman, Digital Image Processing. Prentice-Hall International, Inc., Englewood Cliffs (1996)Google Scholar
  12. 12.
    Lovász, L., Plummer, M.D.: Matching Theory. North Holland, Amsterdam (1986)MATHGoogle Scholar
  13. 13.
    Jiang, W., Er, G., Dai, Q.: Multilayer semantic representation learning for image retrieval. In: International Conf. on Image Processing, vol. 4, pp. 2215–2218 (2004)Google Scholar
  14. 14.
    Jing, F., Li, M., Zhang, H.-J., Zhang, B.: A unified framework for image retrieval using keyword and visual features. IEEE Trans. on Image Processing 14, 979–989 (2005)CrossRefGoogle Scholar
  15. 15.
    Manjunath, B.S., Ohm, J.-R., Vasudevan, V.V., Yamada, A.: Color and texture descritors. IEEE Trans. Circuits Syst. for Video Technol. 11, 703–715 (2001)CrossRefGoogle Scholar
  16. 16.
    Peng, Y.X., Ngo, C.W., Dong, Q.J., Guo, Z.M., Xiao, J.G.: An approach for video retrieval by videoclip. Journal of Software 14(8), 1409–1417 (2003)MATHGoogle Scholar
  17. 17.
    Wan, X.J., Peng, Y.X.: A new retrieval model based on texttiling for document similarity search. Comput. Sci. & Technol 20(4) (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xin Huang
    • 1
  • Shijia Zhang
    • 1
  • Guoping Wang
    • 1
  • Heng Wang
    • 1
  1. 1.Human-Computer Interaction & Multimedia Lab, Department of Computer Science and TechnologyPeking UniversityBeijingP.R. China

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