A Color-Based Interest Operator

  • Marta Penas
  • Linda G. Shapiro
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5716)


In this paper we propose a novel interest operator robust to photometric and geometric transformations. Our operator is closely related to the grayscale MSER but it works on the HSV color space, as opposed to the most popular operators in the literature, which are intensity based. It combines a fine and a coarse overlapped quantization of the HSV color space to find maximally stable extremal regions on each of its components and combine them into a final set of regions that are useful in images where intensity does not discriminate well. We evaluate the performance of our operator on two different applications: wide-baseline stereo matching and image annotation.


interest operators feature matching HSV color space wide-baseline stereo image annotation 


  1. 1.
    Schmid, C., Mohr, R.: Local grayvalue invariants for image retrieval. IEEE Trans. on Pattern Analysis and Machine Intelligence 19(5), 530–535 (1997)CrossRefGoogle Scholar
  2. 2.
    Tuytelaars, T., Gool, L.V.: Content-based image retrieval based on local affinely invariant regions. In: International Conference on Visual Information Systems, pp. 493–500 (1999)Google Scholar
  3. 3.
    Sivic, J., Zisserman, A.: Video google: a test retrieval approach to object matching in videos. In: International Conference on Computer Vision (2003)Google Scholar
  4. 4.
    Schaffalitzky, F., Zisserman, A.: Automated location matching in movies. Computer Vision and Image Understanding 92, 236–264 (2003)CrossRefzbMATHGoogle Scholar
  5. 5.
    Harris, C.: Geometry from visual motion. Active Vision, 263–284 (1992)Google Scholar
  6. 6.
    Torr, P.: Motion segmentation and outlier detection. PhD thesis, University of Oxford (1995)Google Scholar
  7. 7.
    Tuytelaars, T., Gool, L.V.: Matching widely separated views based on affine invariant regions. International Journal on Computer Vision 59(1), 61–85 (2004)CrossRefGoogle Scholar
  8. 8.
    Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. International Journal on Computer Vision 60 (2004)Google Scholar
  9. 9.
    Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: British Machine Video Conference, pp. 384–393 (2002)Google Scholar
  10. 10.
    Kadir, T., Zisserman, A., Brady, M.: An affine invariant salient region detector. In: European Conference on Computer Vision, pp. 404–416 (2004)Google Scholar
  11. 11.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  12. 12.
    Marszałek, M., Schmid, C., Harzallah, H., van de Weijer, J.: Learning object representations for visual object class recognition. In: Visual Recognition Challenge Workshop, in conjunction with ICCV (October 2007)Google Scholar
  13. 13.
    Forssen, P.: Maximally stable colour regions for recognition and matching. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)Google Scholar
  14. 14.
    Van de Weijer, J., Schmid, C.: Coloring local feature extraction. In: European Conference on Computer Vision, pp. 334–348 (2006)Google Scholar
  15. 15.
    Van de Sande, K., Gevers, T., Snoek, C.: Evaluation of color descriptors for object and scene recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)Google Scholar
  16. 16.
    Smith, J.R.: Integrated spatial and feature image system: retrieval, analysis and compression. PhD thesis, Columbia University (1997)Google Scholar
  17. 17.
    Zhang, L., Lin, F., Zang, B.: A CBIR method based on color-spatial feature. In: IEEE Region 10 International Conference TENCON, pp. 166–169 (1999)Google Scholar
  18. 18.
    Huang, C., Yu, S., Zhou, J., Lu, H.: Image retrieval using both color and local spatial feature histograms. In: Int. Conference on Communications, Circuits and Systems, pp. 927–931 (2004)Google Scholar
  19. 19.
    Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2169–2178 (2006)Google Scholar
  20. 20.
    Griffin, G., Holub, A., Perona, P.: Caltech-256 object category dataset. Technical report 7694, California Institute of Technology (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Marta Penas
    • 1
  • Linda G. Shapiro
    • 2
  1. 1.Dpt. of Computer ScienceUniversity of A CoruñaA CoruñaSpain
  2. 2.Dpt. of Computer Science and EngineeringUniversity of WashingtonUS

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