Interest Points via Maximal Self-Dissimilarities

  • Federico TombariEmail author
  • Luigi Di Stefano
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9004)


We propose a novel interest point detector stemming from the intuition that image patches which are highly dissimilar over a relatively large extent of their surroundings hold the property of being repeatable and distinctive. This concept of contextual self-dissimilarity reverses the key paradigm of recent successful techniques such as the Local Self-Similarity descriptor and the Non-Local Means filter, which build upon the presence of similar - rather than dissimilar - patches. Moreover, our approach extends to contextual information the local self-dissimilarity notion embedded in established detectors of corner-like interest points, thereby achieving enhanced repeatability, distinctiveness and localization accuracy.


Interest Point Image Patch JPEG Compression Image Pyramid Repeatable Feature 
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.


  1. 1.
    Moravec, H.: Towards automatic visual obstacle avoidance. In: Proceedings of the International Joint Conference on Artificial Intelligence (1977)Google Scholar
  2. 2.
    Harris, C., Stephens, M.: A combined corner and edge detector. In: Proceedings of the Alvey Vision Conference, pp. 147–151 (1988)Google Scholar
  3. 3.
    Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. Int. J. Comput. Vis. 60, 63–86 (2004)CrossRefGoogle Scholar
  4. 4.
    Shechtman, E., Irani, M.: Matching local self-similarities across images and videos. In: Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR 2007) (2007)Google Scholar
  5. 5.
    Huang, J., You, S., Zhao, J.: Multimodal image matching using self similarity. In: Proceedings of the Workshop on Applied Imagery Pattern Recognition (AIPR) (2011)Google Scholar
  6. 6.
    Maver, J.: Self-similarity and points of interest. Trans. Pattern Anal. Mach. Intell. (PAMI) 32, 1211–1226 (2010)CrossRefGoogle Scholar
  7. 7.
    Buades, A., Coll, B., Morel, J.: A review of image denoising methods, with a new one. Multiscale Model. Simul. 4, 490–530 (2006)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3d transform-domain collaborative filtering. IEEE Tran. Image Process. 16, 1395–1411 (2007)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Kadir, T., Brady, M.: Saliency, scale and image description. Int. J. Comput. Vis. 45, 83–105 (2000)CrossRefGoogle Scholar
  10. 10.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)CrossRefGoogle Scholar
  11. 11.
    Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: Proceedings of the British Machine Vision Conference, BMVC 2002, vol. 1, pp. 384–393 (2002)Google Scholar
  12. 12.
    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (surf). Comput. Vis. Image Underst. 110, 346–359 (2008)CrossRefGoogle Scholar
  13. 13.
    Goferman, S., Zelnik-Manor, L., Tal, A.: Context-aware saliency detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2010) (2010)Google Scholar
  14. 14.
    Borji, A., Itti, L.: Exploiting local and global patch rarities for saliency detection. In: Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR 2012) (2012)Google Scholar
  15. 15.
    Mc Donnel, M.: Box-filtering techniques. Comput. Graph. Image Process. 17, 65–70 (1981)CrossRefGoogle Scholar
  16. 16.
    Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Gool, L.V.: A comparison of affine region detectors. Int. J. Comput. Vis. 65, 43–72 (2005)CrossRefGoogle Scholar
  17. 17.
    Salti, S., Lanza, A., Stefano, L.D.: Keypoints from symmetries by wave propagation. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (2013)Google Scholar
  18. 18.
    Aanæs, H., Dahl, A.L.: Steenstrup Pedersen, K.: Interesting interest points. Int. J. Comput. Vis. 97, 18–35 (2012)CrossRefGoogle Scholar
  19. 19.
    Hel-Or, Y., Hel-Or, H., David, E.: Fast template matching in non-linear tone-mapped images. In: Proceedings of the International Conference on Computer Vision (ICCV) (2011)Google Scholar
  20. 20.
    Barnes, C., Shechtman, E., Goldman, D.B., Finkelstein, A.: The generalized PatchMatch correspondence algorithm. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part III. LNCS, vol. 6313, pp. 29–43. Springer, Heidelberg (2010) CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.DISIUniversity of BolognaBolognaItaly

Personalised recommendations