Image Retrieval Using Weighted Color Co-occurrence Matrix

  • Dong Liang
  • Jie Yang
  • Jin-jun Lu
  • Yu-chou Chang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3567)


Weighted Color Co-occurrence Matrix (WCCM) is introduced as a novel feature for image retrieval. When indexing images with WCCM feature, the similarities of diagonal elements and non-diagonal elements are weighted respectively based on the Isolation Parameters of the query and prototype images. After weighting, the similarity of relevant matches to the query image is strengthened and the similarity of non-relevant matches to the query is weakened. The experiments show the effectiveness of WCCM based method.


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  1. 1.
    Rui, Y., Huang, T.S., Ortega, M., Mehrotra, S.: Relevance feedback: a power tool for interactive content-based image retrieval. IEEE Trans. On Circuits and Systems for Video Technology 8(5), 644–655 (1998)CrossRefGoogle Scholar
  2. 2.
    Flickner, M., Sawhney, J., et al.: Query by Image and Video Content: the QBIC system. IEEE computer 28, 23–32 (1995)Google Scholar
  3. 3.
    He, X., King, O., Ma, W.-Y., Li, M., Zhang, H.-J.: Learning a Semantic Space From User’s Relevance Feedback for Image Retrieval. IEEE Trans. On Circuits and Systems for Video Technolog 13(1), 39–48 (2003)CrossRefGoogle Scholar
  4. 4.
    Kovalev, V., Volmer, S.: Color Co-occurrence Descriptor for Querying-by-Example. Multimedia Modeling, 32–38 (1998)Google Scholar
  5. 5.
    Qiu, G.: Color image indexing using BTC. IEEE Transactions on Image Processing 12(1), 93–101 (2003)CrossRefGoogle Scholar
  6. 6.
    Qiu, G.: Constraint adaptive segmentation for color image coding and content-based retrieval. In: 2001 IEEE Fourth Workshop on Multimedia Signal Processing, pp. 269–274 (2001)Google Scholar
  7. 7.
    Shim, S.-O., Choi, T.-S.: Image Indexing by Modified Color Co-occurrence Matrix. In: IEEE International Conference on Image Processing, vol. 3, pp. 493–496 (2003)Google Scholar
  8. 8.
    Yun, L., Yu-Jin, Z., Yong-Ying, G.: Meaningful Regions Extraction Based on Image Analysis. Chinese Journal Of Computer 23(12), 1313–1319 (2000)Google Scholar
  9. 9.
    Zhang, Y.J., Liu, Z.W., He, Y.: Color-based Image Retrieval using Sub-range Cumulative Histogram. High Technology Letters 4(2), 71–75 (1998)Google Scholar
  10. 10.
    Gevers, T., Smeulders, A.W.M.: PicToSeek: combining color and shape invariant features for image retrieval. IEEE Transactions on Image Processing 9(1), 102–119 (2000)CrossRefGoogle Scholar
  11. 11.
    Ko, Byun, B., Hyeran: Extracting salient regions and learning importance scores in region-based image retrieval. International Journal of Pattern Recognition and Artificial Intelligence 17(8), 1349–1367 (2003)CrossRefGoogle Scholar
  12. 12.
    Hoiem, Derek Sukthankar, Rahul, Schneiderman, H., Huston, L.: Object-based image retrieval using the statistical structure of images. In: Proceedings of the 2004 CVPR, vol. 2, pp. 490–497 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Dong Liang
    • 1
  • Jie Yang
    • 1
  • Jin-jun Lu
    • 1
  • Yu-chou Chang
    • 1
  1. 1.Institute of Image Processing and Pattern RecognitionShanghai Jiao Tong UniversityShanghaiChina

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