Towards scalable representations of object categories: Learning a hierarchy of parts

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2007, Pages

  Fidler, Sanja ^a   Leonardis, Aleš ^a  

^a UNIVERSITY OF LJUBLJANA   (Slovenia)

Author keywords

[No Author keywords available]

Indexed keywords

ROBUST CONTROL; ROBUSTNESS (CONTROL SYSTEMS); STATISTICS;

HIERARCHICAL REPRESENTATION; ROBUST MATCHING; SCALABLE REPRESENTATION; STRUCTURAL FLEXIBILITY; VISUAL INPUT;

OBJECT RECOGNITION;

EID: 35148867545     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2007.383269     Document Type: Conference Paper

References (22)

1. A. Agarwal and B. Triggs. Hyperfeatures - multilevel local coding for visual recognition. 2005. 1, 2
2. Y. Amit and D. Geman. A computational model for visual selection. Neural Comp., 11(7):1691-1715, 1999. 1, 2
3. H. B. Barlow. Conditions for versatile learning, helmholtz's unconscious inference, and the task of perception. Vision Research, 30:1561-1571, 1990. 1
4. A. Califano and R. Mohan. Multidimensional indexing for recognizing visual shapes. PAMI, 16(4):373-392, 1994. 1, 2
5. S. Edelman and N. Intrator. Towards structural systematicity in distributed, statically bound visual representations. Cognitive Science, 27:73-110, 2003. 1
6. G. J. Ettinger. Hierarchical object recognition using libraries of parameterized model sub-parts. AITR-963, 1987. 1
7. S. Fidler, G. Berginc, and A. Leonardis. Hierarchical statistical learning of generic parts of object structure. In CVPR, pages 182-189, 2006. 1, 2, 3, 4
8. F. Fleuret and D. Geman. Coarse-to-fine face detection. IJCV, 41(1/2):85-107, 2001. 1, 2
9. K. Fukushima, S. Miyake, and T. Ito. Neocognitron: a neural network model for a mechanism of visual pattern recognition. IEEE SMC, 13(3):826-834, 1983. 1, 2
10. S. Geman, D. Potter, and Z. Chi. Composition systems. Quarterly of App. Math., Vol. 60(Nb. 4):707-736, 2002. 2
11. F.-J. Huang and Y. LeCun. Large-scale learning with svm and convolutional nets for generic object categorization. In CVPR, pages 284-291, 2006. 1, 2
12. S. Krempp, D. Geman, and Y. Amit. Sequential learning of reusable parts for object detection. TR, CS Johns Hopkins, 2002. 1
13. B. W. Mel and J. Fiser. Minimizing binding errors using learned conjunctive features. Neural Computation, 12(4):731-762, 2000. 1
14. K. Mikolajczyk, B. Leibe, and B. Schiele. Multiple object class detection with a generative model. In CVPR06, pages 26-36. 2, 6
15. J. Mutch and D. G. Lowe. Multiclass object recognition with sparse, localized features. In CVPR06, pages 11-18, 2006. 2, 7
16. M. Riesenhuber and T. Poggio. Hierarchical models of object recognition in cortex. Nature Neurosc., 2(11):1019-1025, Nov. 1999. 2
17. E. Rolls and G. Deco. Computational Neuroscience of Vision. Oxford Univ. Press, 2002. 1
18. F. Scalzo and J. H. Piater. Statistical learning of visual feature hierarchies. In Workshop on Learning, CVPR, 2005. 1
19. T. Serre, L. Wolf, and T. Poggio. Object recognition with features inspired by visual cortex. In CVPR (2), pages 994-1000, 2005. 1, 2
20. E. Sudderth, A. Torralba, W. Freeman, and A. Willsky. Learning hierarchical models of scenes, objects, and parts. In ICCV, pages 1331-1338, 2005. 1, 2
21. J. K. Tsotsos. Analyzing vision at the complexity level. BBS, 13(3):423-469, 1990. 1
22. S. Ullman and B. Epshtein. Visual Classification by a Hierarchy of Extended Features. Towards Category-Level Object Recognition. Springer-Verlag, 2006. 1, 2