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Knowing a Good Feature When You See It: Ground Truth and Methodology to Evaluate Local Features for Recognition

  • Andrea Vedaldi
  • Haibin Ling
  • Stefano Soatto
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 285)

Abstract

While the majority of computer vision systems are based on representing images by local features, the design of the latter has been so far mostly empirical. In this Chapter we propose to tie the design of local features to their systematic evaluation on a realistic ground-truthed dataset. We propose a novel mathematical characterisation of the co-variance properties of the features which accounts for deviation from the usual idealised image affine (de)formation model. We propose novel metrics to evaluate the features and we show how these can be used to automatically design improved features.

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References

  1. 1.
    Chum, O., Matas, J.: Matching with PROSAC – progressive sample consensus. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (2005)Google Scholar
  2. 2.
    Fraundorfer, F., Bischof, H.: A novel performance evaluation method of local detectors on non-planar scenes. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (2005)Google Scholar
  3. 3.
    Hertz, T., Bar-Hillel, A., Weinshall, D.: Learning distance functions for image retrieval. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (2004)Google Scholar
  4. 4.
    Lepetit, V., Lagger, P., Fua, P.: Randomized trees for real-time keypoint recognition. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (2005)Google Scholar
  5. 5.
    Ling, H., Jacobs, D.W.: Deformation invariant image matching. In: Proceedings of the International Conference on Computer Vision (2005)Google Scholar
  6. 6.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 2(60), 91–110 (2004)CrossRefGoogle Scholar
  7. 7.
    Ma, Y., Soatto, S., Kosecka, J., Sastry, S.S.: An Invitation to 3-D Vision. Springer, Heidelberg (2003b)Google Scholar
  8. 8.
    Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: Proceedings of the Brithish Machine Vision Conference (2002)Google Scholar
  9. 9.
    Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Van Gool, L.: A comparison of affine region detectors. International Journal of Computer Vision 1(60), 63–86 (2004)CrossRefGoogle Scholar
  10. 10.
    Moreels, P., Perona, P.: Evaluation of features detectors and descriptors based on 3D objects. In: Proceedings of the International Conference on Computer Vision (2005)Google Scholar
  11. 11.
    Piqueres, J.V.: The persistence of ignorance (2006), http://www.ignorancia.org/
  12. 12.
    Rockett, P.I.: Performance assesment of feature detection algorithms: A methodology and case study on corner detectors. Transaction on Image Processing 12(12) (2003)Google Scholar
  13. 13.
    Roth, S., Black, M.J.: On the spatial statistics of optical flow. In: Proceedings of the International Conference on Computer Vision (2005)Google Scholar
  14. 14.
    Vedaldi, A.: A ground-truthed dataset for evaluation and learning of viewpoint invariant features (2008), http://vision.ucla.edu/gtvpi
  15. 15.
    Vedaldi, A., Fulkerson, B.: VLFeat: An open and portable library of computer vision algorithms (2008), http://www.vlfeat.org/
  16. 16.
    Vedaldi, A., Soatto, S.: Features for recognition: Viewpoint invariance for non-planar scenes. In: Proceedings of the International Conference on Computer Vision (2005)Google Scholar
  17. 17.
    Winder, S.A.J., Brown, M.: Learning local image descriptors. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (2007)Google Scholar
  18. 18.
    Zhou, S.K., Shao, J., Georgescu, B., Comaniciu, D.: BoostMotion: Boosting a discriminative similarity function for motion estimation. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (2006)Google Scholar
  19. 19.
    Zhuang, H., Sudhakar, R.: Simultaneous rotation and translation fitting of two 3-D point sets. Transaction on Systems, Man, and Cybernetics 27(1) (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Andrea Vedaldi
    • 1
  • Haibin Ling
    • 2
  • Stefano Soatto
    • 1
  1. 1.University of California at Los AngelesLos AngelesUSA
  2. 2.Temple UniversityPhiladelphiaUSA

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