Image classification using random forests and ferns

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2007, Pages

  Bosch, Anna ^a   Zisserman, Andrew ^b   Muñoz, Xavier ^a  

^a UNIVERSITY OF GIRONA   (Spain)

^b UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

CALTECH; INTERNATIONAL CONFERENCES;

ARTIFICIAL INTELLIGENCE; COMPUTER NETWORKS; COMPUTER VISION; IMAGE ANALYSIS; IMAGE CLASSIFICATION; IMAGE PROCESSING; IMAGE SEGMENTATION; LEARNING SYSTEMS; RANDOM PROCESSES; SUPPORT VECTOR MACHINES;

CLASSIFICATION (OF INFORMATION);

EID: 50649101132     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2007.4409066     Document Type: Conference Paper

References (27)

1. Y. Amit and D. Geman. Shape quantization and recognition with randomized trees. Neural Computation, 9:1545-1588, 1997.
2. Y. Amit, D. Geman, and K. Wilder. Joint induction of shape features and tree classifiers. IEEE PAMI, 19(11):1300-1305, 1997.
3. A. Berg, T. Berg, and J. Malik. Shape matching and object recognition using low distortion correspondence. CVPR, 2005.
4. A. Bosch, A. Zisserman, and X. Muñoz. Scene classification via plsa. ECCV, 2006.
5. A. Bosch, A. Zisserman, and X. Muñoz. Representing shape with a spatial pyramid kernel. CIVR, 2007.
6. L. Breiman. Random forests. Machine Learning, 45:5-32, 2001.
7. O. Chum and A. Zisserman. An exemplar model for learning object classes. CVPR, 2007.
8. G. Csurka, C. Bray, C. Dance, and L. Fan. Visual categorization with bags of keypoints. In Workshop on Statistical Learning in Computer Vision, ECCV, pages 1-22, 2004.
9. N. Dalal and B. Triggs. Histogram of oriented gradients for human detection. CVPR, 2005.
10. L. Fei-Fei, R. Fergus, and P. Perona. Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. In IEEE CVPR Workshop of Generative Model Based Vision, 2004.
11. A. Frome, Y. Singer, and J. Malik. Image retrieval and classification using local distance functions. NIPS, 2006.
12. K. Grauman and T. Darrell. The pyramid match kernel: Discriminative classification with sets of image features. ICCV, 2005.
13. G. Griffin, A. Holub, and P. Perona. Caltech 256 object category dataset. Technical Report UCB/CSD-04-1366, California Institute of Technology, 2007.
14. A. Hegerath, T. Deselaers, and H. Ney. Patch-based object recognition using discriminatively trained gaussian mixtures. BMVC., 2006.
15. I. Laptev. Improvements of object detection using boosted histograms. BMVC., 2006.
16. S. Lazebnik, C. Schmid, and J. Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. CVPR, 2006.
17. V. Lepetit and P. Fua. Keypoint recognition using randomized trees. IEEE PAMI, 2006.
18. Y. Lin, T. Liu, and C. Fuh. Local ensemble kernel learning for object category recognition. CVPR, 2007.
19. D. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 60(2):91-110, 2004.
20. F. Moosmann, B. Triggs, and F. Jurie. Fast discriminative visual codebooks using randomized clustering forests. NIPS, 2006.
21. M. Ozuysal, P. Fua, and V. Lepetit. Fast keypoint recognition in ten lines of code. CVPR, 2007.
22. V. Perronnin, C. Dance, G. Csurka, andM. Bressan. Adapted vocabularies for generic visual categorization. ECCV, 2006.
23. J. Sivic and A. Zisserman. Video Google: A text retrieval approach to object matching in videos. ICCV, 2003.
24. J. Winn and A. Criminisi. Object class recognition at a glance. CVPR, 2006.
25. J. Winn and J. Shotton. The layout consistent random field for recognizing and segmenting partially occluded objects. CVPR, 2006.
26. H. Zhang, A. Berg, M. Maire, and J. Malik. SVM-KNN: Discriminative nearest neighbor classification for visual category recognition. CVPR, 2006.
27. J. Zhang, M. Marsza?ek, and C. Lazebnik, S. Schmid. Local features and kernels for classification of texture and object categories: a comprehensive study. IJCV, 2007.