Advertisement

Voting by Grouping Dependent Parts

  • Pradeep Yarlagadda
  • Antonio Monroy
  • Björn Ommer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6315)

Abstract

Hough voting methods efficiently handle the high complexity of multi-scale, category-level object detection in cluttered scenes. The primary weakness of this approach is however that mutually dependent local observations are independently voting for intrinsically global object properties such as object scale. All the votes are added up to obtain object hypotheses. The assumption is thus that object hypotheses are a sum of independent part votes. Popular representation schemes are, however, based on an overlapping sampling of semi-local image features with large spatial support (e.g. SIFT or geometric blur). Features are thus mutually dependent and we incorporate these dependences into probabilistic Hough voting by presenting an objective function that combines three intimately related problems: i) grouping of mutually dependent parts, ii) solving the correspondence problem conjointly for dependent parts, and iii) finding concerted object hypotheses using extended groups rather than based on local observations alone. Experiments successfully demonstrate that state-of-the-art Hough voting and even sliding windows are significantly improved by utilizing part dependences and jointly optimizing groups, correspondences, and votes.

Keywords

Training Image Object Detection Query Image Correspondence Problem Query 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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Lehmann, B.L.A., van Gool, L.: Prism principled implicit shape model. In: BMVC (2008)Google Scholar
  2. 2.
    Ahuja, N., Todorovic, S.: Connected segmentation tree: A joint representation of region layout and hierarchy. In: CVPR (2008)Google Scholar
  3. 3.
    Amit, Y., Geman, D.: A computational model for visual selection. Neural Computation (1999)Google Scholar
  4. 4.
    Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: From contours to regions: An empirical evaluation. In: CVPR (2009)Google Scholar
  5. 5.
    Berg, A.C., Berg, T.L., Malik, J.: Shape matching and object recognition using low distortion correspondence. In: CVPR, pp. 26–33 (2005)Google Scholar
  6. 6.
    Boiman, O., Shechtman, E., Irani, M.: In defense of nearest-neighbor based image classification. In: CVPR (2008)Google Scholar
  7. 7.
    Bouchard, G., Triggs, B.: Hierarchical part-based visual object categorization. In: CVPR, pp. 710–715 (2005)Google Scholar
  8. 8.
    Carneiro, G., Lowe, D.: Sparse flexible models of local features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3953, pp. 29–43. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    Comaniciu, D., Ramesh, V., Meer, P.: The variable bandwidth mean shift and data-driven scale selection. In: ICCV, pp. 438–445 (2001)Google Scholar
  10. 10.
    Crandall, D.J., Felzenszwalb, P.F., Huttenlocher, D.P.: Spatial priors for part-based recognition using statistical models. In: CVPR, pp. 10–17 (2005)Google Scholar
  11. 11.
    Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: ECCV, Workshop Stat. Learn. in Comp. Vis. (2004)Google Scholar
  12. 12.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR, pp. 886–893 (2005)Google Scholar
  13. 13.
    Estrada, F.J., Fua, P., Lepetit, V., Susstrunk, S.: Appearance-based keypoint clustering. In: CVPR (2009)Google Scholar
  14. 14.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Pictorial structures for object recognition. IJCV 61(1) (2005)Google Scholar
  15. 15.
    Fergus, R., Perona, P., Zisserman, A.: Object class recognition by unsupervised scale-invariant learning. In: CVPR, pp. 264–271 (2003)Google Scholar
  16. 16.
    Ferrari, V., Jurie, F., Schmid, C.: From images to shape models for object detection. IJCV (2009)Google Scholar
  17. 17.
    Fidler, S., Boben, M., Leonardis, A.: Similarity-based cross-layered hierarchical representation for object categorization. In: CVPR (2008)Google Scholar
  18. 18.
    Gall, J., Lempitsky, V.: Class-specific hough forests for object detection. In: CVPR (2009)Google Scholar
  19. 19.
    Hough, P.: Method and means for recognizing complex patterns. U.S. Patent 3069654 (1962)Google Scholar
  20. 20.
    Lampert, C.H., Blaschko, M.B., Hofmann, T.: Beyond sliding windows: Object localization by efficient subwindow search. In: CVPR (2008)Google Scholar
  21. 21.
    Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: CVPR, pp. 2169–2178 (2006)Google Scholar
  22. 22.
    Leibe, B., Leonardis, A., Schiele, B.: Robust object detection with interleaved categorization and segmentation. IJCV 77(1-3), 259–289 (2008)CrossRefGoogle Scholar
  23. 23.
    Lowe, D.: Object recognition from local scale-invariant features. In: ICCV (1999)Google Scholar
  24. 24.
    Maire, M., Arbelaez, P., Fowlkes, C., Malik, J.: Using contours to detect and localize junctions in natural images. In: CVPR (2008)Google Scholar
  25. 25.
    Maji, S., Malik, J.: Object detection using a max-margin hough transform. In: CVPR (2009)Google Scholar
  26. 26.
    Medioni, G., Tang, C., Lee, M.: Tensor voting: Theory and applications. In: RFIA (2000)Google Scholar
  27. 27.
    Ommer, B., Buhmann, J.: Learning the compositional nature of visual object categories for recognition. PAMI 32(3), 501–516 (2010)Google Scholar
  28. 28.
    Ommer, B., Malik, J.: Multi-scale object detection by clustering lines. In: ICCV (2009)Google Scholar
  29. 29.
    Opelt, A., Pinz, A., Zisserman, A.: Incremental learning of object detectors using a visual shape alphabet. In: CVPR, pp. 3–10 (2006)Google Scholar
  30. 30.
    Shotton, J., Blake, A., Cipolla, R.: Contour-based learning for object detection. In: ICCV (2005)Google Scholar
  31. 31.
    Sivic, J., Russell, B.C., Efros, A.A., Zisserman, A., Freeman, W.T.: Discovering objects and their localization in images. In: ICCV, pp. 370–377 (2005)Google Scholar
  32. 32.
    Sudderth, E.B., Torralba, A.B., Freeman, W.T., Willsky, A.S.: Learning hierarchical models of scenes, objects, and parts. In: ICCV, pp. 1331–1338 (2005)Google Scholar
  33. 33.
    Viola, P.A., Jones, M.J.: Robust real-time face detection. IJCV 57(2), 137–154 (2004)CrossRefGoogle Scholar
  34. 34.
    Williams, C., Allan, M.: On a connection between object localization with a generative template of features and pose-space prediction methods. Technical report, University of Edinburg, Edinburg (2006)Google Scholar
  35. 35.
    Zhu, Q.H., Wang, L.M., Wu, Y., Shi, J.B.: Contour context selection for object detection: A set-to-set contour matching approach. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 774–787. Springer, Heidelberg (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Pradeep Yarlagadda
    • 1
  • Antonio Monroy
    • 1
  • Björn Ommer
    • 1
  1. 1.Interdisciplinary Center for Scientific ComputingUniversity of HeidelbergGermany

Personalised recommendations