Dominant Color-Based Indexing Method for Fast Content-Based Image Retrieval

  • Ahmed Talib
  • Massudi Mahmuddin
  • Husniza Husni
  • Loay E. George
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 265)


Content-based image retrieval is an active research area in image processing and computer vision. Color represents an important feature in CBIR applications, thus many color descriptors were proposed. Sequential search is one of the common drawbacks of most color descriptors especially in large databases. In this paper, dominant colors of an image are indexed to avoid sequential search in the database. Dominant colors in query image are used independently to find images that containing similar colors to create reduced search space instead of the whole database search space. This will speed up the retrieval process in addition to improve the accuracy of color descriptors. Experimental results show effectiveness of the proposed color indexing method in reducing the search space to less than 25% without degradation the accuracy.


Color indexing Dominant colors MPEG-7 descriptors RGB color space Database search space 


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  1. 1.
    Penatti, O.A.B., Valle, E., Torres, R.D.S.: Comparative Study of Global Color and Texture Descriptors for Web Image Retrieval. Journal of Visual Communication and Image Representation (2012)CrossRefGoogle Scholar
  2. 2.
    Talib, A., Mahmuddin, M., Husni, H., George, L.E.: A weighted dominant color descriptor for content-based image retrieval. Journal of Visual Communication and Image Representation 24, 345–360 (2013)CrossRefGoogle Scholar
  3. 3.
    Yamada, A., Pickering, M., Jeannin, S., Jens, L.C.: MPEG-7 Visual Part of Experimentation Model Version 9.0-Part 3 Dominant Color. ISO/IEC JTC1/SC29/WG11/N3914, Pisa (2001)Google Scholar
  4. 4.
    Yang, N.-C., Chang, W.-H., Kuo, C.-M., Li, T.-H.: A fast MPEG-7 dominant color extraction with new similarity measure for image retrieval. Journal of Visual Communication and Image Representation 19(2008), 92–105 (2008)CrossRefGoogle Scholar
  5. 5.
    Kiranyaz, S., Birinci, M., Gabbouj, M.: Perceptual color descriptor based on spatial distribution: A top-down approach. Journal of Image and Vision Computing 28(2010), 1309–1326 (2010)CrossRefGoogle Scholar
  6. 6.
    Kiranyaz, S., Birinci, M., Gabbouj, M.: Perceptual Color Descriptors. Foveon, Inc., Sigma Corp., San Jose, California, USA (2012)Google Scholar
  7. 7.
    Talib, A., Mahmuddin, M., Husni, H., George, L.E.: Efficient, Compact, and Dominant Color Correlogram Descriptors for Content-based Image Retrieval. Presented at the MMEDIA 2013: Fifth International Conference on Advances in Multimedia, Venice, Italy, April 22-26 (2013)Google Scholar
  8. 8.
    Wong, K.M., Po, L.M., Cheung, K.W.: A compact and efficient color descriptor for image retrieval. In: Proceedings of IEEE International Conference on Multimedia and Expo (ICME 2007), Beijing, China, pp. 611–614 (2007)Google Scholar
  9. 9.
    Jouili, S., Tabbone, S.: Hypergraph-based image retrieval for graph-based representation. Pattern Recognition 45, 4054–4068 (2012)CrossRefGoogle Scholar
  10. 10.
    Kunttu, I., Lepistö, L., Rauhamaa, J., Visa, A.: Image correlogram in image database indexing and retrieval. In: Proceedings of 4th European Workshop on Image Analysis for Multimedia Interactive Services, London, UK, April 9-11, pp. 88–91 (2003)Google Scholar
  11. 11.
    Hou, A.L., Zhao, L.-Q., Shi, D.-C.: Garment image retrieval based on multi-features. In: International Conference on Computer, Mechatronics, Control and Electronic Engineering (CMCE), pp. 194–197 (2010)Google Scholar
  12. 12.
    Gervautz, M., Purgathofer, W.: A simple method for color quantization: Octree quantization. In: New Trends in Computer Graphics, pp. 219–231. Springer (1988)CrossRefGoogle Scholar
  13. 13.
    Yildizer, E., Balci, A.M., Jarada, T.N., Alhajj, R.: Integrating wavelets with clustering and indexing for effective content-based image retrieval. Knowledge-Based Systems 31, 55–66 (2012)CrossRefGoogle Scholar
  14. 14.
    Deng, Y., Manjunath, B.S., Kenney, C., Moore, M.S., Shin, H.: An efficient color representation for image retrieval. IEEE Trans. Image Process 10(1), 140–147 (2001)CrossRefGoogle Scholar
  15. 15.
    Babu, G.P., Mehtre, B.M., Kankanhalli, M.S.: Color indexing for efficient image retrieval. Multimedia Tools and Applications 1, 327–348 (1995)CrossRefGoogle Scholar
  16. 16.
    Sudhamani, M., Venugopal, C.: Grouping and indexing color features for efficient image retrieval. International Journal of Applied Mathematics and Computer Sciences i3, 150–155 (2007)Google Scholar
  17. 17.
    Stehling, R.D.O., Nascimento, M.A., Falcão, A.X.: Cell histograms versus color histograms for image representation and retrieval. Knowledge and Information Systems 5(3), 315–336 (2003)CrossRefGoogle Scholar
  18. 18.
    Khan, F.S., Rao, M.A., van de Weijer, J., Bagdanov, A.D., Vanrell, M., Lopez, A.: Color Attributes for Object Detection. In: Twenty-Fifth IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ahmed Talib
    • 1
    • 2
  • Massudi Mahmuddin
    • 1
  • Husniza Husni
    • 1
  • Loay E. George
    • 3
  1. 1.Computer Science Dept., School of ComputingUniversity Utara MalaysiaSintokMalaysia
  2. 2.IT Dept., Technical College of ManagementFoundation of Technical EducationBaghdadIraq
  3. 3.Computer Science Dept., College of ScienceBaghdad UniversityBaghdadIraq

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