Similarity Measure of the Visual Features Using the Constrained Hierarchical Clustering for Content Based Image Retrieval

  • Sang Min Yoon
  • Holger Graf
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5359)


In this paper, we present a methodology on how to measure the visual similarity between a query image and hierarchically represented image databases for content based image retrieval. The images in database are hierarchically summarized and classified by recovered extrinsic camera parameters as well as constrained agglomerative clustering methods. The constrained agglomerative hierarchical image clustering method whose strategy is to extract a multi-level partitioning and grouping of multiple images is used for balancing the hierarchical trees and summarization. The visual codebooks which are hierarchically quantized in the clusters are used to calculate the similarity measure with a query image’s visual features. Our proposed visual similarity measure and summarization of image data provide a very efficient way for searching and retrieving the images that have similar visual contents and geometrical location.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Lim, J.H., Li, J., Mulhem, P., Tian, Q.: Content-based summarization for personal image library. In: The Proceeding of ACM/IEEE Joint Conference on Digital Library, pp. 393 (2003)Google Scholar
  2. 2.
    Marée, R., Geurts, P., Wehenkel, L.: Content-based Image Retrieval by Indexing Random Subwindows with Randomized Trees. In: The Proceeding of 8th Asian Conference on Computer Vision, pp. 611–620 (2007)Google Scholar
  3. 3.
    Jurie, F., Triggs, B.: Creating efficent codebook four visual recognition. In: The Proceeding of International Conference of Computer Vision, pp. 604–610 (2005)Google Scholar
  4. 4.
    Leung, T., Malik, J.: Representing and recognizing the visual appearance of materials using threedimensional textons. Internation Journal of Computer Vision 43(1), 29–44 (2001)CrossRefzbMATHGoogle Scholar
  5. 5.
    Zhang, J., Marszalek, M., Lazebnik, S., Schmid, C.: Local features and kernels for classification of texture and object categories: A comprehensive study. International Journal of Computer Vision (2006)Google Scholar
  6. 6.
    Winn, J., Criminisi, A., Minka, T.: Object categorization by learned universal visual dictionary. In: Proceeding of Inernational Conference of Computer Vision, pp. 1800–1807 (2005)Google Scholar
  7. 7.
    Csurka, G., Dance, C., Fan, L., Williamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: European Conference on Computer Vision 04 workshop on Statistical Learning in CV, pp. 59–74 (2004)Google Scholar
  8. 8.
    Comaniciu, D., Meer, P.: Mean Shift: A robust approach toward feature space analysis. IEEE Transaction on PAMI 24, 603–619 (2002)CrossRefGoogle Scholar
  9. 9.
    Cormark, R.: A review of classification. Journal of the Royal Statistical Society Series A 134, 321–367 (1971)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Lowe, D.: Distinctive Image Features from Scale-Invariant Keypoints. Internationl Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  11. 11.
    Schaffalitzky, F., Ziserman, A.: Multi-view matching for unordered image sets, or How do I organize my holiday snaps? In: The Proceeding of European Conference on Computer Vision, pp. 414–431 (2002)Google Scholar
  12. 12.
    Krishnamachari, S., Abdel-Mottaleb, M.: Hierarchical Clustering Algorithm for fast Image RetrievalGoogle Scholar
  13. 13.
    Nowark, E., Jurie, F.: Learning Visual Similarity Measure for Comparing Never Seen object. In: The Proceeding of Computer Vision of Pattern Recognition, pp. 1–8 (2007)Google Scholar
  14. 14.
    Eidenberger, H., Breiteneder, C.: An experimental study on the performance of visual information retrieval similarity models. In: IEEE Workshop on Multimedia Signal Processing, pp. 233–236 (2002)Google Scholar
  15. 15.
    Chen, Y., Wong, E.: Augmented Image Histogram for Image and Video Similarity Search. In: Proceeding of SPIE Storage and Retrieval for Image and Video Database, pp. 523–532 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Sang Min Yoon
    • 1
  • Holger Graf
    • 1
  1. 1.GRIS, TU-Darmstadt, Germany, ZGDV, Computer Graphics CenterDarmstadtGermany

Personalised recommendations