HOP: Hierarchical object parsing

2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009

Volumn , Issue , 2009, Pages 802-809

  Kokkinos, Iasonas ^b   Yuille, Alan ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b INRIA SACLAY   (France)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION;

COMBINATORIAL COMPLEXITY; COMPOSITION RULE; HIERARCHICAL OBJECT REPRESENTATION; HIERARCHICAL OBJECTS; INFERENCE METHODS; OBJECT LOCALIZATION; OBJECT STRUCTURE; SIMPLE STRUCTURES;

OBJECT DETECTION;

EID: 70450181234     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPRW.2009.5206639     Document Type: Conference Paper

References (32)

1. S. Agrawal and D. Roth. Learning a Sparse Representation for Object Detection. In ECCV, 2002.
2. N. Ahuja and S. Todorovic. Learning the taxonomy and models of categories present in arbitrary images. In ICCV, 2007.
3. Y. Chen, L. Zhu, C. Lin, A. L. Yuille, and H. Zhang. Rapid inference on a novel and/or graph for object detection, segmentation and parsing. In NIPS, 2007.
4. P. Felzenszwalb and D. Huttenlocher. Pictorial Structures for Object Recognition. IJCV, 2005.
5. P. Felzenszwalb and J. Schwartz. Hierarchical Matching of Deformable Shapes. In CVPR, 2007.
6. R. Fergus, P. Perona, and A. Zisserman. Object Class Recognition by Unsupervised Scale-Invariant Learning. In CVPR, 2003.
7. V. Ferrari, L. Fevrier, F. Jurie, and C. Schmid. Groups of adjacent contour segments for object detection. Technical report, INRIA, 2006.
8. V. Ferrari, T. Tuytelaars, and L. V. Gool. Object Detection by Contour Segment Networks. In ECCV, 2006.
9. S. Fidler and A. Leonardis. Towards Scalable Representations of Object Categories. In CVPR, 2006.
10. K. S. Fu. Syntactic Pattern Recognition. Prentice-Hall, 1974.
11. E. Grimson. Object Recognition by Computer. MIT Press, 1991.
12. F. Han and S. C. Zhu. Bottom-Up/Top-Down Image Parsing by Attribute Graph Grammar. In ICCV, 2005.
13. D. Jacobs and M. Lindenbaum. Special Issue of Perceptual Organization in Computer Vision. IEEE T. PAMI, 25(4), 2003.
14. Y. Jin and S. Geman. Context and Hierarchy in a Probabilistic Image Model. In CVPR, 2006.
15. I. Kokkinos and A. Yuille. Unsupervised Learning of Object DeformationModels. In ICCV, 2007.
16. C. H. Lampert, M. B.Blaschko, and T. Hofmann. Beyond sliding windows: Object localization by efficient subwindow search. In CVPR, 2008.
17. B. Leibe, A. Leonardis, and B. Schiele. Combined Object Categorization and Segmentation with an Implicit Shape Model. In ECCV, 2004.
18. D. Lowe. Perceptual Organization and Visual Recognition. Kluwer, 1984.
19. D. Martin, C. Fowlkes, and J.Malik. Learning to Detect Natural Image Boundaries. IEEE T. PAMI, 26(5):530-549, 2004.
20. P. Moreels, M. Maire, and P. Perona. Recognition by probabilistic hypothesis construction. In ECCV, page 55, 2004.
21. B. Ommer and J. M. Buhmann. Learning the Compositional Nature of Visual Objects. In CVPR, 2007.
22. A. Opelt, A. Pinz, and A. Zisserman. Boundary-fragment-model for object detection. In CVPR, 2006.
23. P. Felzenszwalb and A. McAllester. The generalized A Architecture. Journal of Artificial Intelligence Research, 2007.
24. J. Pearl. Heuristics. Addison-Wesley, 1984.
25. S. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, 2003.
26. J. Shotton, A. Blake, and R. Cipolla. Contour-based learning for object recognition. In ICCV, 2005.
27. A. Storkey and C.Williams. Image modelling with position-encoding dynamic trees. IEEE T. PAMI, 25(7), 2003.
28. Z. Tu, X. Chen, A. Yuille, and S. Zhu. Image Parsing: Unifying Segmentation, Detection, and Recognition. IJCV, 63(2):113-140, 2005.
29. L. Zelnik and P. Perona. Self-tuning spectral clustering. In NIPS, 2005.
30. L. Zhu, C. Lin, H. Huang, Y. Chen, and A. Yuille. Unsupervised Structure Learning: Hierarchical Recursive Composition, Suspicious Coincidence and Competitive Exclusion. In ECCV, 2008.
31. Q. Zhu, L. Wang, Y. Wu, and J. Shi. Contour Context Selection for Object Detection: A Set-to-Set Contour Matching Approach. In ECCV, 2008.
32. S. C. Zhu and D. Mumford. Quest for a Stochastic Grammar of Images. Foundations and Trends in Comp. Gr. and Vis., 2007.