A two level approach for scene recognition

Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005

Volumn I, Issue , 2005, Pages 688-695

  Lu, Le ^a   Toyama, Kentaro ^b   Hager, Gregory D ^a  

^a Johns Hopkins University   (United States)

^b MICROSOFT RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE PROCESSING; IMAGE SEGMENTATION; MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD ESTIMATION; PROBABILITY DENSITY FUNCTION; PROBLEM SOLVING;

LDA CLASSIFIERS; LOOPY BELIEF PROPAGATION; PROBABILITY DENSITY RESPONSE MAPS (PDRM);

PATTERN RECOGNITION;

EID: 33745119067     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/cvpr.2005.51     Document Type: Conference Paper

References (35)

1. S. Aksoy and R. Haralick, Feature Normalization and Likelihood-Based Similarity Measures for Image Retrieval, Pattern Recognition Letters, 22(5):563-582, 2001.
2. L. Breiman, Bagging Predictors, Machine Learning, 24(2):123-140, 1996.
3. M. Boutell, J. Luo and C. Brown, Learning spatial configuration models using modified Dirichlet priors, Workshop on Statistical Relational Learning, 2004.
4. L. Chen, H. Liao, M. Ko, J. Lin, and G. Yu, A new LDA-based face recognition system which can solve the small sample size problem. Journal of Pattern Recognition, 33(10):1713-1726, 2000.
5. O. Cula and K. Dana, Compact representation of bidirectional texture functions, CVPR I:1041-1047, 2001.
6. J. De Bonet, P. Viola, A non-parametric multi-scale statistical model for natural images, NIPS, 1997.
7. A. Dempster, N. Laird and D. Rubin, Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society, Series B, 39:1-38, 1977.
8. R. Fergus, P. Perona and A. Zisserman, Object class recognition by unsupervised scale-invariant learning, CVPR, 2003.
9. G. Golub and C. Van Loan, Matrix Computations, Johns Hopkins University Press, 1996.
10. R. Haralick, K. Shanmugam, and I. Dinstein, Texture features for image classification. IEEE Trans. on System, Man and Cybernatic, 1973.
11. X. He, R. Zemel and M. Carreira-Perpin̄án, Multiscale Conditional Random Fields for Image Labeling, CVPR, 2004.
12. T. K. Ho, The random subspace method for constructing decision forests, IEEE Trans. on PAMI, 20(8):832-844, 1998.
13. T. Kadir and M. Brady, Scale, saliency and image description, IJCV, 45(2):83105, 2001.
14. S. Kumar and M. Hebert, Man-made structure detection in natural images using a causal multiscale random field, CVPR, 1:119-126, 2003.
15. S. Kumar, A. C. Loui and M. Hebert, An Observation-Constrained Generative Approach for Probabilistic Classification of Image Regions, Image and Vision Computing, 21:87-97, 2003.
16. T. Leung and J. Malik, Representing and recognizing the visual appearance of materials using three-dimensional textons, IJCV, 2001.
17. T. Lindeberg, Principles for automatic scale selection. Handbook on Computer Vision and Applications, 2:239-274, Academic Press, Boston, 1999.
18. Le Lu, Gregory D. Hager and Laurent Younes, A Three Tiered Approach for Articulated Object Action Modeling and Recognition, NIPS, 2004.
19. J. Luo, A. Singhal, S. Etz, and R. Gray, A computational approach to determination of main subject regions in photographic images, Image Vision Computing, 22(3):227-241, 2004.
20. J. Malik, S. Belongie, T. Leung and J. Shi Contour and texture analysis for image segmentation, Int Journal of Computer Vision, 43(1):7-27, 2001.
21. A. Ng, M. Jordan and Y. Weiss, On Spectral Clustering: Analysis and an algorithm, NIPS, 2001.
22. N. Serrano, A. Savakis and J. Luo, Improved scene classification using efficient low-level features and semantic cues, Pattern Recognition 37(9): 1773-1784, 2004.
23. A. Singhal, J. Luo and W. Zhu, Probabilistic Spatial Context Models for Scene Content Understanding, CVPR, 2003.
24. M. Szummer and R. W. Picard, Indoor-outdoor image classification, IEEE Int. Workshop Content-Based Access Image Video Databases, 1998.
25. A. Torralba, Contextual priming for object detection, Int Journal of Computer Vision, 53(2): 169-191, 2003.
26. A. Vailaya, M. Figueiredo, A. Jain and H.-J. Zhang, Image classification for content-based indexing, IEEE Trans. Image Processing, 10(1): 117-130, 2001.
27. M. Varma and A. Zisserman, Classifying images of materials: achieving viewpoint and illumination independence, ECCV, 2002.
28. X. Wang and X. Tang, Random sampling LDA for face recognition, CVPR, 2004.
29. X. Wang and X. Tang, Dual-space linear discriminant analysis for face recognition, CVPR, 2004.
30. M. Weber, M. Welling and P. Perona, unsupervised learning of models for recognition, ECCV, 2000.
31. A. Webb, Statistical Pattern Recognition, John Wiley & Sons, 2002.
32. Y. Wu, Q. Tian, T. Huang, Discriminant-EM algorithm with application to image retrieval, CVPR, I:222-227, 2000.
33. J. Yedidia, W. T. Freeman and Y. Weiss, Understanding belief propagation and its generalizations, IJCAI, 2001.
34. M. Zhu and T. Hastie, Feature extraction for non-parametric discriminant analysis, JCGS, 12(1):101-120, 2003.
35. S.C. Zhu, Y.N. Wu, and D. Mumford, Minimax entropy principle and its applications to texture modeling, Neural Computation, 9:1627-1660, 1997.