Object recognition via local patch labelling

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3635 LNAI, Issue , 2005, Pages 1-21

  Bishop, Christopher M ^a   Ulusoy, Ilkay ^b  

^a MICROSOFT RESEARCH   (United Kingdom)

^b MIDDLE EAST TECHNICAL UNIVERSITY   (Turkey)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL GEOMETRY; COMPUTER VISION; DATA REDUCTION; IMAGE SEGMENTATION; LEARNING SYSTEMS;

IMAGE LABELING; LOCAL PATCH LABELING; NON-RIGID DEFORMATIONS;

OBJECT RECOGNITION;

EID: 33645993558     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11559887_1     Document Type: Conference Paper

References (14)

1. K. Barnard, P. Duygulu, D. Forsyth, N. Freitas, D. Blei, and M. I. Jordan. Matching words and pictures. Journal of Machine Learning Research, 3:1107-1135, 2003.
2. C. M. Bishop. Neural Networks for Pattern Recognition. Oxford University Press, 1995.
3. G. Csurka, C. R. Dance, L. Fan, J. Willamowski, and C. Bray. Visual categorization with bags of keypoints. In Workshop on Statistical Learning in Computer Vision, ECCV, 2004.
4. G. Dorko and C. Schmid. Selection of scale invariant parts for object class recognition. In ICCV, 2003.
5. R. Fergus, P. Perona, and A. Zisserman. Object class recognition by unsupervised scale invariant learning. In CVPR, 2003.
6. T. Kadir and M. Brady. Scale, saliency and image description. International Journal of Computer Vision, 45(2):83-105, 2001.
7. D. Lowe. Distinctive image features from scale invariant keypoints. International Journal of Computer Vision, 60(2):91-110, 2004.
8. D. J. C. MacKay. Probable networks and plausible predictions - a review of practical Bayesian methods for supervised neural networks. 6(3):469-505, 1995.
9. K. Mikolajczyk and C. Schmid. Scale and affine invariant interest point detectors. International Journal of Computer Vision, 60:63-86, 2004.
10. I. T. Nabney. Netlab Algorithms for Pattern Recognition. Springer, 2004.
11. R. Neal P. Dayan, G. E. Hinton and R. S. Zemel. The helmholtz machine. Neural Computation, pages 1022-1037, 1995.
12. J. Pearl. Probabilistic Reasoning in Intelligent Systems: Net- works of Plausible Inference. Morgan Kaufmann Publishers, 1998.
13. M. Vidal-Naquet and S. Ullman. Object recognition with informative features and linear classification. In ICCV, 2003.
14. L. Xie and P. Perez. Slightly supervised learning of part-based appearance models. In IEEE Workshop on Learning in CVPR, 2004.