Advertisement

Improving Local Features by Dithering-Based Image Sampling

  • Christos VarytimidisEmail author
  • Konstantinos Rapantzikos
  • Yannis Avrithis
  • Stefanos Kollias
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9004)

Abstract

The recent trend of structure-guided feature detectors, as opposed to blob and corner detectors, has led to a family of methods that exploit image edges to accurately capture local shape. Among them, the W\(\alpha \)SH detector combines binary edge sampling with gradient strength and computational geometry representations towards distinctive and repeatable local features. In this work, we provide alternative, variable-density sampling schemes on smooth functions of image intensity based on dithering. These methods are parameter-free and more invariant to geometric transformations than uniform sampling. The resulting detectors compare well to the state-of-the-art, while achieving higher performance in a series of matching and retrieval experiments.

Keywords

Image Coverage Gradient Strength Sift Descriptor Retrieval Experiment Binary Edge 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

This work is supported by the National GSRT-funded project 09SYN-72-922 “IS-HELLEANA : Intelligent System for HELLEnic Audiovisual National Aggregator”, http://www.helleana.gr, 2011-2014.

References

  1. 1.
    Mikolajczyk, K., Schmid, C.: An affine invariant interest point detector. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part I. LNCS, vol. 2350, pp. 128–142. Springer, Heidelberg (2002) CrossRefGoogle Scholar
  2. 2.
    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. (CVIU) 110, 346–359 (2008)CrossRefGoogle Scholar
  3. 3.
    Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide-baseline stereo from maximally stable extremal regions. Image Vis. Comput. 22, 761–767 (2004)CrossRefGoogle Scholar
  4. 4.
    Varytimidis, C., Rapantzikos, K., Avrithis, Y.: W\(\alpha \)SH: weighted \(\alpha \)-shapes for local feature detection. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 788–801. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  5. 5.
    Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Gool, L.: A comparison of affine region detectors. Int. J. Comput. Vis. (IJCV) 65, 43–72 (2005)CrossRefGoogle Scholar
  6. 6.
    Mikolajczyk, K., Zisserman, A., Schmid, C.: Shape recognition with edge-based features. Br. Mach. Vis. Conf. (BMVC) 2, 779–788 (2003)Google Scholar
  7. 7.
    Rapantzikos, K., Avrithis, Y., Kollias, S.: Detecting regions from single scale edges. In: Kutulakos, K.N. (ed.) ECCV 2010 Workshops, Part I. LNCS, vol. 6553, pp. 298–311. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  8. 8.
    Zitnick, C., Ramnath, K.: Edge foci interest points. In: International Conference on Computer Vision (ICCV), pp. 359–366 (2011)Google Scholar
  9. 9.
    Avrithis, Y., Rapantzikos, K.: The medial feature detector: Stable regions from image boundaries. In: International Conference on Computer Vision (ICCV), pp. 1724–1731 (2011)Google Scholar
  10. 10.
    Floyd, R.W., Steinberg, L.: An adaptive algorithm for spatial gray-scale. Proc. Soc. Inf. Disp. 17, 75–77 (1976)Google Scholar
  11. 11.
    Ostromoukhov, V.: A simple and efficient error-diffusion algorithm. In: Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques, pp. 567–572. ACM (2001)Google Scholar
  12. 12.
    Zhou, B., Fang, X.: Improving mid-tone quality of variable-coefficient error diffusion using threshold modulation. In: ACM Transactions on Graphics (TOG). vol. 22, pp. 437–444. ACM (2003)Google Scholar
  13. 13.
    Gu, S., Zheng, Y., Tomasi, C.: Critical nets and beta-stable features for image matching. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) Computer Vision – ECCV 2010. LNCS, vol. 6313, pp. 663–676. Springer, Berlin (2010) CrossRefGoogle Scholar
  14. 14.
    Fergus, R., Perona, P., Zisserman, A.: Object class recognition by unsupervised scale-invariant learning. In: 2003 Proceedings of the IEEE Conference on Computer Society Computer Vision and Pattern Recognition, vol. 2, pp. Ii-264. IEEE (2003)Google Scholar
  15. 15.
    Yang, Y., Wernick, M., Brankov, J.: A fast approach for accurate content-adaptive mesh generation. IEEE Trans. Image Proc. 12, 866–881 (2003)CrossRefzbMATHMathSciNetGoogle Scholar
  16. 16.
    Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results (2003). http://www.pascal-network.org/challenges/VOC/voc2007/workshop/index.html
  17. 17.
    Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Object retrieval with large vocabularies and fast spatial matching. In: Computer Vision and Pattern Recognition (CVPR), pp. 1–8 (2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Christos Varytimidis
    • 1
    Email author
  • Konstantinos Rapantzikos
    • 1
  • Yannis Avrithis
    • 1
  • Stefanos Kollias
    • 1
  1. 1.National Technical University of AthensAthensGreece

Personalised recommendations