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Category Independent Object Proposals

  • Ian Endres
  • Derek Hoiem
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6315)

Abstract

We propose a category-independent method to produce a bag of regions and rank them, such that top-ranked regions are likely to be good segmentations of different objects. Our key objectives are completeness and diversity: every object should have at least one good proposed region, and a diverse set should be top-ranked. Our approach is to generate a set of segmentations by performing graph cuts based on a seed region and a learned affinity function. Then, the regions are ranked using structured learning based on various cues. Our experiments on BSDS and PASCAL VOC 2008 demonstrate our ability to find most objects within a small bag of proposed regions.

Keywords

Appearance Model Object Region Boost Decision Tree Hierarchical Segmentation Occlusion Boundary 
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. 1.
    Farhadi, A., Endres, I., Hoiem, D., Forsyth, D.: Describing objects by their attributes. In: CVPR (2009)Google Scholar
  2. 2.
    Lampert, C.H., Nickisch, H., Harmeling, S.: Learning to detect unseen object classes by between-class attribute transfer. In: CVPR (2009)Google Scholar
  3. 3.
    Goodale, M.A., Milner, A.D., Jakobson, L.S., Carey, D.P.: A neurological dissociation between perceiving objects and grasping them. Nature 349, 154–156 (2000)CrossRefGoogle Scholar
  4. 4.
    Hoiem, D., Stein, A.N., Efros, A.A., Hebert, M.: Recovering occlusion boundaries from an image. In: ICCV (2007)Google Scholar
  5. 5.
    Martin, D., Fowlkes, C., Malik, J.: Learning to find brightness and texture boundaries in natural images. In: NIPS (2002)Google Scholar
  6. 6.
    Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge, VOC 2008 Results (2008), http://www.pascal-network.org/challenges/VOC/voc2008/workshop/index.html
  7. 7.
    Viola, P., Jones, M.J.: Robust real-time face detection. IJCV 57 (2004)Google Scholar
  8. 8.
    Felzenszwalb, P., McAllester, D., Ramanan, D.: A discriminatively trained, multiscale, deformable part model. In: CVPR (2008)Google Scholar
  9. 9.
    Leibe, B., Leonardis, A., Schiele, B.: Robust object detection with interleaved categorization and segmentation. IJCV 77, 259–289 (2008)CrossRefGoogle Scholar
  10. 10.
    Maji, S., Malik, J.: Object detection using a max-margin hough transform. In: CVPR, pp. 1038–1045. IEEE Computer Society, Los Alamitos (2009)Google Scholar
  11. 11.
    Chum, O., Zisserman, A.: An exemplar model for learning object classes. In: CVPR (2007)Google Scholar
  12. 12.
    Vedaldi, A., Gulshan, V., Varma, M., Zisserman, A.: Multiple kernels for object detection. In: ICCV (2009)Google Scholar
  13. 13.
    Gu, C., Lim, J., Arbelaez, P., Malik, J.: Recognition using regions. In: CVPR, pp. 1030–1037 (2009)Google Scholar
  14. 14.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. PAMI 22 (2000)Google Scholar
  15. 15.
    Felzenszwalb, P., Huttenlocher, D.: Efficient graph-based image segmentation. IJCV 59 (2004)Google Scholar
  16. 16.
    Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: From contours to regions: An empirical evaluation. In: CVPR, pp. 2294–2301 (2009)Google Scholar
  17. 17.
    Sharon, E., Galun, M., Sharon, D., Basri, R., Brandt, A.: Hierarchy and adaptivity in segmenting visual cues. Nature (2006)Google Scholar
  18. 18.
    Hoiem, D., Efros, A.A., Hebert, M.: Geometric context from a single image. In ICCV (2005)Google Scholar
  19. 19.
    Malisiewicz, T., Efros, A.A.: Improving spatial support for objects via multiple segmentations. In: BMVC (2007)Google Scholar
  20. 20.
    Russell, B.C., Efros, A.A., Sivic, J., Freeman, W.T., Zisserman, A.: Using multiple segmentations to discover objects and their extent in image collections. In: CVPR (2006)Google Scholar
  21. 21.
    Stein, A., Stepleton, T., Hebert, M.: Towards unsupervised whole-object segmentation: Combining automated matting with boundary detection. In: CVPR (2008)Google Scholar
  22. 22.
    Gould, S., Fulton, R., Koller, D.: Decomposing a scene into geometric and semantically consistent regions. In: ICCV (2009)Google Scholar
  23. 23.
    Walther, D., Koch, C.: 2006 special issue: Modeling attention to salient proto-objects. Neural Networks 19, 1395–1407 (2006)zbMATHCrossRefGoogle Scholar
  24. 24.
    Liu, T., Sun, J., Zheng, N., Tang, X., Shum, H.: Learning to detect a salient object. In: CVPR, pp. 1–8 (2007)Google Scholar
  25. 25.
    Alexe, B., Deselaers, T., Ferrari, V.: What is an object? In: CVPR (2010)Google Scholar
  26. 26.
    Carreira, J., Sminchisescu, C.: Constrained parametric min cuts for automatic object segmentation. In: CVPR (2010)Google Scholar
  27. 27.
    Rother, C., Kolmogorov, V., Blake, A.: “grabcut”: interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. 23, 309–314 (2004)CrossRefGoogle Scholar
  28. 28.
    Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: LabelMe: a database and web-based tool for image annotation. Technical report, MIT (2005)Google Scholar
  29. 29.
    Hoiem, D., Efros, A.A., Hebert, M.: Recovering surface layout from an image. IJCV 75, 151–172 (2007)CrossRefGoogle Scholar
  30. 30.
    Szummer, M., Kohli, P., Hoiem, D.: Learning crfs using graph cuts. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 582–595. Springer, Heidelberg (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ian Endres
    • 1
  • Derek Hoiem
    • 1
  1. 1.Department of Computer ScienceUniversity of Illinois at Urbana-Champaign 

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