Segmentation Using Saturation Thresholding and Its Application in Content-Based Retrieval of Images

  • A. Vadivel
  • M. Mohan
  • Shamik Sural
  • A. K. Majumdar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3211)


We analyze some of the visual properties of the HSV (Hue, Saturation and Value) color space and develop an image segmentation technique using the results of our analysis. In our method, features are extracted either by choosing the hue or the intensity as the dominant property based on the saturation value of a pixel. We perform content-based image retrieval by object-level matching of segmented images. A freely usable web-enabled application has been developed for demonstrating our work and for performing user queries.


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  1. 1.
    Carson, C., et al.: Blobworld: A System for Region-based Image Indexing and Retrieval. In: Third Int. Conf. on Visual Information Systems (June 1999)Google Scholar
  2. 2.
    Chen, J., Pappas, T.N., Mojsilovic, A., Rogowitz, B.: Adaptive Image Segmentation Based on Color and Texture. In: IEEE Conf. on Image Processing (2002)Google Scholar
  3. 3.
    Deng, Y., Manjunath, B.S.: Unsupervised Segmentation of Color-texture Regions in Image and video. IEEE Trans. on PAMI 23, 800–810 (2001)Google Scholar
  4. 4.
    Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley & Sons, New York (1990)Google Scholar
  5. 5.
    Ma, W.Y., Manjunath, B.S.: NeTra: A Toolbox for Navigating Large Image Databases. In: IEEE Int. Conf. on Image Processing, pp. 568–571 (1997)Google Scholar
  6. 6.
    Niblack, W., et al.: The QBIC Project: Querying Images by Content using Color Texture and Shape. In: SPIE Int. Soc. Opt. Eng., In Storage and Retrieval for Image and Video Databases, vol. 1908, pp. 173–187 (1993)Google Scholar
  7. 7.
    Ortega, M., et al.: Supporting Ranked Boolean Similarity Queries in MARS. IEEE Trans. on Knowledge and Data Engineering 10, 905–925 (1998)CrossRefGoogle Scholar
  8. 8.
    Randen, T., Husoy, J.H.: Texture Segmentation using Filters with Optimized Energy Separation. IEEE Trans. on Image Processing 8, 571–582 (1999)CrossRefGoogle Scholar
  9. 9.
    Smeulders, A.W.M., et al.: Content Based Image Retrieval at the End of the Early Years. IEEE Trans. on PAMI 22, 1–32 (2000)Google Scholar
  10. 10.
    Smith, J.R., Chang, S.-F.: VisualSeek: A Fully Automated Content based Image Query System. In: ACM Multimedia Conf. Boston, MA (1996)Google Scholar
  11. 11.
    Stockman, G., Shapiro, L.: Computer Vision. Prentice Hall, New Jersey (2001)Google Scholar
  12. 12.
    Wang, J.Z., Li, J., Wiederhold, G.: SIMPLIcity: Semantics-sensitive Integrated Matching for Picture Libraries. IEEE Trans. on PAMI 23 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • A. Vadivel
    • 1
  • M. Mohan
    • 1
  • Shamik Sural
    • 2
  • A. K. Majumdar
    • 1
  1. 1.Department of Computer Science and EngineeringIndian Institute of TechnologyKharagpurIndia
  2. 2.School of Information TechnologyIndian Institute of TechnologyKharagpurIndia

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