The truth about cats and dogs

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2011, Pages 1427-1434

  Parkhi, Omkar M ^a   Vedaldi, Andrea ^b   Jawahar, C V ^a   Zisserman, Andrew ^b  

^a INTERNATIONAL INSTITUTE OF INFORMATION TECHNOLOGY   (India)

^b UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BAG OF WORDS; DEFORMABLE OBJECT; FLEXIBLE OBJECT; OBJECT CATEGORIES; OBJECT CLASS; OBJECT DETECTORS; SPECIFIC INFORMATION; STATE-OF-THE-ART PERFORMANCE; TEMPLATE-BASED; WHOLE-BODY;

ANIMALS; DEFORMATION; IMAGE SEGMENTATION; OBJECT RECOGNITION; TEACHING;

IMAGE TEXTURE;

EID: 84856635163     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2011.6126398     Document Type: Conference Paper

References (40)

1. N. Ahuja and S. Todorovic. Connected segmentation tree-A joint representation of region layout and hierarchy. In Proc. CVPR, 2008.
2. E. Borenstein and S. Ullman. Class-specific, top-down segmentation. In Proc. ECCV, pages 109-124, 2002.
3. E. Borenstein and S. Ullman. Learning to segment. In Proc. ECCV, volume 3, pages 315-328, 2004.
4. L. Bourdev and J. Malik. Poselets: Body part detectors trained using 3D human pose annotations. In Proc. ICCV, 2009.
5. Y. Boykov, H. Veksler, and R. Zabih. Fast approximate energy minimization via graph cuts. PAMI, 23(11), 2001.
6. N. Dalal and B. Triggs. Histogram of Oriented Gradients for Human Detection. In Proc. CVPR, volume 2, pages 886-893, 2005.
7. P. Dollár, Z. Tu, and S. Belongie. Supervised learning of edges and object boundaries. In Proc. CVPR, 2006.
8. M. Everingham, L. Van Gool, C. K. I.Williams, J.Winn, and A. Zisserman. The PASCAL Visual Object Classes Challenge 2010 (VOC2010) Results, 2010.
9. M. Everingham, L. Van Gool, C. K. I.Williams, J.Winn, and A. Zisserman. The PASCAL Visual Object Classes (VOC) challenge. IJCV, 88(2):303-338, Jun 2010.
10. P. Felzenszwalb and D. Huttenlocher. Pictorial structures for object recognition. IJCV, 61(1), 2005.
11. P. F. Felzenszwalb, R. B. Grishick, D. McAllester, and D. Ramanan. Object detection with discriminatively trained part based models. IEEE PAMI, 2009.
12. R. Fergus, L. Fei-Fei, P. Perona, and A. Zisserman. Learning object categories from Google's image search. In Proc. ICCV, 2005.
13. F. Fleuret and D. Geman. Stationary features and cat detection. Journal of Machine Learning Research, 9, 2008.
14. J. Fritsch, S. Lang, M. Kleinehagenbrock, G. A. Fink, and G. Sagerer. Improving adaptive skin color segmentation by incorporating results from face detection. In IEEE Workshop on Robot and Human Interactive Communication, 2002.
15. J. M. Gonfaus, X. Boix, J. van de Weijer, A. D. Bagdanov, J. Serrat, and J. Gonzàlez. Harmony potentials for joint classification and segmentation. In Proc. CVPR, 2011.
16. S. Gould, R. Fulton, and D. Koller. Decomposing a scene into geometric and semantically consistent regions. In Proc. ICCV, 2009.
17. C. Gu, J. J. Lim, P. Arbeláez, and J. Malik. Recognition using regions. In Proc. CVPR, 2009.
18. H. Harzallah, C. Schmid, F. Jurie, and A. Gaidon. Classification aided two stage localization. Presented at the ECCV VOC 2008 Workshop, 2008.
19. G. Heitz and D. Koller. Learning spatial context: Using stuff to find things. In Proc. ECCV, 2008.
20. D. Hoeim, C. Rother, and J. M. Winn. 3D layout CRF for multi-view object class recognition and segmentation. In Proc. CVPR, 2008.
21. M. P. Kumar, P. H. S. Torr, and A. Zisserman. OBJ CUT. In Proc. CVPR, volume 1, pages 18-25, 2005.
22. L. Ladicky, P. Sturgess, K. Alahari, C. Russell, and P. Torr. Where, what and how many? combining object detectors and CRFs. In Proc. ECCV, 2010.
23. C. H. Lampert, M. B. Blaschko, and T. Hofmann. Efficient subwindow search: A branch and bound framework for object localization. IEEE PAMI, 2009.
24. I. Laptev. Improvements of object detection using boosted histograms. In Proc. BMVC, 2006.
25. D. Larlus and F. Jurie. Combining appearance models and Markov random fields for category level object segmentation. In Proc. CVPR, 2008.
26. S. Lazebnik, C. Schmid, and J. Ponce. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. In Proc. CVPR, 2006.
27. B. Leibe, A. Leonardis, and B. Schiele. Combined object categorization and segmentation with an implicit shape model. In Workshop on Statistical Learning in Computer Vision, ECCV, May 2004.
28. A. Levin and Y. Weiss. Learning to combine bottom-up and top-down segmentation. In Proc. ECCV, 2006.
29. F. Li, J. Carreira, and C. Sminchisescu. Object recognition as ranking holistic figure-ground hypotheses. In Proc. CVPR, 2010.
30. D. R. Martin, C. Fowlkes, and J. Malik. Learning to detect natural image boundaries using local brightness, color, and texture cues. PAMI, 1, 2004.
31. T. Ojala, M. Pietikäinen, and D. Harwood. A comparative study of texture measures with classification based on feature distributions. Pattern Recognition, 29, 1996.
32. M. Prasad, A. Zisserman, A. W. Fitzgibbon, M. P. Kumar, and P. H. S. Torr. Learning class-specific edges for object detection and segmentation. In Proc. ICVGIP, Dec 2006.
33. A. Rabinovich, A. Vedaldi, C. Galleguillos, E. Wiewiora, and S. Belongie. Objects in context. In Proc. ICCV, 2007.
34. D. Ramanan. Using segmentation to verify object hypotheses. In Proc. CVPR, 2007.
35. C. Rother, V. Kolmogorov, and A. Blake. Grabcut: interactive foreground extraction using iterated graph cuts. Proc. ACM SIGGRAPH, 23(3):309-314, 2004.
36. B. C. Russel, A. A. Efros, J. Sivic, W. T. Freeman, and A. Zisserman. Using multiple segmentations to discover objects and their extent in image collections. In Proc. CVPR, 2006.
37. J. Shotton, J. Winn, C. Rother, and A. Criminisi. Texton-Boost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation. In Proc. ECCV, 2006.
38. A. Vedaldi, V. Gulshan, M. Varma, and A. Zisserman. Multiple kernels for object detection. In Proc. ICCV, 2009.
39. K. Yu, T. Zhang, and Y. Gong. Nonlinear learning using local coordinate coding. In Proc. NIPS, 2009.
40. W. Zhang, J. Sun, and X. Tang. Cat head detection-how to effectively exploit shape and texture features. In Proc. ECCV, 2008.