Skip to main content

Dog Breed Classification Using Part Localization

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNIP,volume 7572)

Abstract

We propose a novel approach to fine-grained image classification in which instances from different classes share common parts but have wide variation in shape and appearance. We use dog breed identification as a test case to show that extracting corresponding parts improves classification performance. This domain is especially challenging since the appearance of corresponding parts can vary dramatically, e.g., the faces of bulldogs and beagles are very different. To find accurate correspondences, we build exemplar-based geometric and appearance models of dog breeds and their face parts. Part correspondence allows us to extract and compare descriptors in like image locations. Our approach also features a hierarchy of parts (e.g., face and eyes) and breed-specific part localization. We achieve 67% recognition rate on a large real-world dataset including 133 dog breeds and 8,351 images, and experimental results show that accurate part localization significantly increases classification performance compared to state-of-the-art approaches.

Keywords

  • Face Detection
  • Query Image
  • Color Histogram
  • Part Localization
  • Sift 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.

References

  1. Spady, T.C., Ostrander, E.A.: Canine behavioral genetics: Pointing out the phenotypes and herding up the genes. AJHG 82(1), 10–18 (2008)

    CrossRef  Google Scholar 

  2. Branson, S., Wah, C., Schroff, F., Babenko, B., Welinder, P., Perona, P., Belongie, S.: Visual Recognition with Humans in the Loop. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 438–451. Springer, Heidelberg (2010)

    CrossRef  Google Scholar 

  3. Nilsback, M.E., Zisserman, A.: Automated flower classification over a large number of classes. In: Proc. 6th Indian Conf. on Computer Vision, Graphics and Image Processing, pp. 722–729 (2008)

    Google Scholar 

  4. Farrell, R., Oza, O., Zhang, N., Morariu, V., Darrell, T., Davis, L.: Birdlets: Subordinate categorization using volumetric primitives and pose-normalized appearance. In: Proc. ICCV (2011)

    Google Scholar 

  5. Belhumeur, P.N., Chen, D., Feiner, S.K., Jacobs, D.W., Kress, W.J., Ling, H., Lopez, I., Ramamoorthi, R., Sheorey, S., White, S., Zhang, L.: Searching the World’s Herbaria: A System for Visual Identification of Plant Species. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 116–129. Springer, Heidelberg (2008)

    CrossRef  Google Scholar 

  6. Belhumeur, P.N., Jacobs, D.W., Kriegman, D.J., Kumar, N.: Localizing parts of faces using a consensus of exemplars. In: Proc. CVPR (2011)

    Google Scholar 

  7. Csurka, G., Dance, C.R., Fan, L., Willamowski, J.: Visual categorization with bags of keypoints. In: Work. on Stat. Learning in Comp. Vis., ECCV, pp. 1–22 (2004)

    Google Scholar 

  8. Jurie, F., Triggs, B.: Creating efficient codebooks for visual recognition. In: Proc. ICCV (2005)

    Google Scholar 

  9. Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: Proc. CVPR, pp. 2169–2178 (2006)

    Google Scholar 

  10. Gehler, P., Nowozin, S.: On feature combination for multiclass object classification. In: Proc. CVPR (2009)

    Google Scholar 

  11. Wang, Z., Hu, Y., Chia, L.-T.: Image-to-Class Distance Metric Learning for Image Classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 706–719. Springer, Heidelberg (2010)

    CrossRef  Google Scholar 

  12. Deselaers, T., Ferrari, V.: Visual and semantic similarity in imagenet. In: Proc. CVPR (2011)

    Google Scholar 

  13. Sadeghi, M.A., Farhadi, A.: Recognition using visual phrases. In: Proc. CVPR (2011)

    Google Scholar 

  14. Yao, B., Khosla, A., Fei-Fei, L.: Combining randomization and discrimination for fine-grained image categorization. In: Proc. CVPR (2011)

    Google Scholar 

  15. Bourdev, L., Maji, S., Brox, T., Malik, J.: Detecting People Using Mutually Consistent Poselet Activations. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part VI. LNCS, vol. 6316, pp. 168–181. Springer, Heidelberg (2010)

    CrossRef  Google Scholar 

  16. Parkhi, O., Vedaldi, A., Zisserman, A., Jawahar, C.: Cats and dogs. In: Proc. CVPR (2012)

    Google Scholar 

  17. Viola, P., Jones, M.: Robust real-time object detection. IJCV 57, 137–154 (2001)

    CrossRef  Google Scholar 

  18. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proc. CVPR, vol. 1, pp. 886–893 (2005)

    Google Scholar 

  19. Parkhi, O., Vedaldi, A., Jawahar, C.V., Zisserman, A.: The truth about cats and dogs. In: Proc. ICCV (2011)

    Google Scholar 

  20. Cristinacce, D., Cootes, T.: Feature detection and tracking with constrained local models. In: Proc. BMVC, pp. 929–938 (2006)

    Google Scholar 

  21. Milborrow, S., Nicolls, F.: Locating Facial Features with an Extended Active Shape Model. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 504–513. Springer, Heidelberg (2008)

    CrossRef  Google Scholar 

  22. Saragih, J.M., Lucey, S., Cohn, J.F.: Face alignment through subspace constrained mean-shifts. In: Proc. ICCV (2009)

    Google Scholar 

  23. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV 20 (2004)

    Google Scholar 

  24. Kumar, N., Berg, A.C., Belhumeur, P.N., Nayar, S.K.: Attribute and simile classifiers for face verification. In: Proc. ICCV (2009)

    Google Scholar 

  25. Yin, Q., Tang, X., Sun, J.: An associate-predict model for face recognition. In: Proc. CVPR, pp. 497–504 (2011)

    Google Scholar 

  26. Arca, S., Campadelli, P., Lanzarotti, R.: A face recognition system based on automatically determined facial fiducial points. Pattern Recognition 39, 432–443 (2006)

    MATH  CrossRef  Google Scholar 

  27. Campadelli, P., Lanzarotti, R., Lipori, G.: Precise eye localization through a general-to-specific model definition. In: Proc. BMVC (2006)

    Google Scholar 

  28. Vidaldi, A., Zisserman, A.: Image classification practical (2011), http://www.robots.ox.ac.uk/~vgg/share/practical-image-classification.htm

  29. Vedaldi, A., Gulshan, V., Varma, M., Zisserman, A.: Multiple kernels for object detection. In: Proc. ICCV, pp. 606–613 (2009)

    Google Scholar 

  30. Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., Gong, Y.: Locality-constrained linear coding for image classification. In: Proc. CVPR, pp. 3360–3367 (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Liu, J., Kanazawa, A., Jacobs, D., Belhumeur, P. (2012). Dog Breed Classification Using Part Localization. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds) Computer Vision – ECCV 2012. ECCV 2012. Lecture Notes in Computer Science, vol 7572. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33718-5_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-33718-5_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33717-8

  • Online ISBN: 978-3-642-33718-5

  • eBook Packages: Computer ScienceComputer Science (R0)