Advertisement

TriCoS: A Tri-level Class-Discriminative Co-segmentation Method for Image Classification

  • Yuning Chai
  • Esa Rahtu
  • Victor Lempitsky
  • Luc Van Gool
  • Andrew Zisserman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7572)

Abstract

The aim of this paper is to leverage foreground segmentation to improve classification performance on weakly annotated datasets – those with no additional annotation other than class labels. We introduce TriCoS, a new co-segmentation algorithm that looks at all training images jointly and automatically segments out the most class-discriminative foregrounds for each image. Ultimately, those foreground segmentations are used to train a classification system.

TriCoS solves the co-segmentation problem by minimizing losses at three different levels: the category level for foreground/background consistency across images belonging to the same category, the image level for spatial continuity within each image, and the dataset level for discrimination between classes.

In an extensive set of experiments, we evaluate the algorithm on three benchmark datasets: the UCSD-Caltech Birds-200-2010, the Stanford Dogs, and the Oxford Flowers 102. With the help of a modern image classifier, we show superior performance compared to previously published classification methods and other co-segmentation methods.

Keywords

Ground Truth Training Image Foreground Object Multiple Kernel Learning Fisher Vector 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Alexe, B., Deselaers, T., Ferrari, V.: What is an object? In: CVPR (2010)Google Scholar
  2. 2.
    Arandjelović, R., Zisserman, A.: Smooth object retrieval using a bag of boundaries. In: ICCV (2011)Google Scholar
  3. 3.
    Blake, A., Rother, C., Brown, M., Pérez, P., Torr, P.: Interactive Image Segmentation Using an Adaptive GMMRF Model. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004, Part I.. LNCS, vol. 3021, pp. 428–441. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  4. 4.
    Boykov, Y., Jolly, M.-P.: Interactive graph cuts for optimal boundary and region segmentation of objects in n-d images. In: ICCV (2001)Google Scholar
  5. 5.
    Carreira, J., Sminchisescu, C.: Constrained parametric min-cuts for automatic object segmentation. In: CVPR (2010)Google Scholar
  6. 6.
    Chai, Y., Lempitsky, V., Zisserman, A.: Bicos: A bi-level co-segmentation method for image classification. In: ICCV (2011)Google Scholar
  7. 7.
    Chatfield, K., Lempitsky, V., Vedaldi, A., Zisserman, A.: The devil is in the details: an evaluation of recent feature encoding methods. In: British Machine Vision Conference (2011)Google Scholar
  8. 8.
    Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR (2009)Google Scholar
  9. 9.
    Endres, I., Hoiem, D.: Category Independent Object Proposals. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 575–588. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  10. 10.
    Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge 2011 (VOC 2011) Results (2011), http://www.pascal-network.org/challenges/VOC/voc2011/workshop/index.html
  11. 11.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. IJCV, 59(2) (2004)Google Scholar
  12. 12.
    Galleguillos, C., Babenko, B., Rabinovich, A., Belongie, S.: Weakly Supervised Object Localization with Stable Segmentations. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 193–207. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  13. 13.
    Kanan, C., Cottrell, G.W.: Robust classification of objects, faces, and flowers using natural image statistics. In: CVPR (2010)Google Scholar
  14. 14.
    Khan, F.S., van de Weijer, J., Badganov, A.D., Vanrell, M.: Portmanteau vocabularies for multi-cue image representation. In: NIPS (2011)Google Scholar
  15. 15.
    Khosla, A., Jayadevaprakash, N., Yao, B., Fei-Fei, L.: Novel dataset for fine-grained image categorization. In: First Workshop on Fine-Grained Visual Categorization, CVPR (2011)Google Scholar
  16. 16.
    Nilsback, M.-E., Zisserman, A.: Delving into the whorl of flower segmentation. In: BMVC (2007)Google Scholar
  17. 17.
    Nilsback, M.E., Zisserman, A.: Automated flower classification over a large number of classes. In: ICVGIP (2008)Google Scholar
  18. 18.
    Perronnin, F., Sánchez, J., Mensink, T.: Improving the Fisher Kernel for Large-Scale Image Classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 143–156. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  19. 19.
    Rahtu, E., Kannala, J., Salo, M., Heikkilä, J.: Segmenting Salient Objects from Images and Videos. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 366–379. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  20. 20.
    Rother, C., Kolmogorov, V., Blake, A.: ”grabcut”: interactive foreground extraction using iterated graph cuts. ACM Trans. Graph, 23(3) (2004)Google Scholar
  21. 21.
    Rother, C., Minka, T.P., Blake, A., Kolmogorov, V.: Cosegmentation of image pairs by histogram matching - incorporating a global constraint into mrfs. In: CVPR (2006)Google Scholar
  22. 22.
    Uijlings, J.R.R., Smeulders, A.W.M., Scha, R.J.H.: What is the spatial extent of an object? In: CVPR, pp. 770–777 (2009)Google Scholar
  23. 23.
    van de Sande, K., Uijlings, J., Gevers, T., Smeulders, A.: Segmentation as selective search for object recognition. In: ICCV (2011)Google Scholar
  24. 24.
    Wang, J., Yang, J., Yu, K., Lv, F., Huang, T.S., Gong, Y.: Locality-constrained linear coding for image classification. In: CVPR (2010)Google Scholar
  25. 25.
    Welinder, P., Branson, S., Mita, T., Wah, C., Schroff, F., Belongie, S., Perona, P.: Caltech-UCSD Birds 200. Technical Report CNS-TR-2010-001, California Institute of Technology (2010)Google Scholar
  26. 26.
    Yao, B., Khosla, A., Li, F.-F.: Combining randomization and discrimination for fine-grained image categorization. In: CVPR (2011)Google Scholar
  27. 27.
    Zhang, J., Marszalek, M., Lazebnik, S., Schmid, C.: Local features and kernels for classification of texture and object categories: A comprehensive study. International Journal of Computer Vision 73(2), 213–238 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yuning Chai
    • 1
  • Esa Rahtu
    • 2
  • Victor Lempitsky
    • 3
  • Luc Van Gool
    • 1
  • Andrew Zisserman
    • 4
  1. 1.Computer Vision GroupETH ZurichSwitzerland
  2. 2.Machine Vision GroupUniversity of OuluFinland
  3. 3.YandexRussia
  4. 4.Visual Geometry GroupUniversity of OxfordUnited Kingdom

Personalised recommendations