Unsupervised learning of visual taxonomies

26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR

Volumn , Issue , 2008, Pages

  Bart, Evgeniy ^a   Porteous, Ian ^b   Perona, Pietro ^a   Welling, Max ^b  

^a CALIFORNIA INSTITUTE OF TECHNOLOGY   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BAYESIAN NETWORKS; COMPUTER VISION; FEATURE EXTRACTION; IMAGE PROCESSING; IMAGING SYSTEMS; IMAGING TECHNIQUES; PATTERN RECOGNITION; STATISTICAL METHODS; TAXONOMIES;

IMAGE CATEGORIZATION; IMAGE REPRESENTATIONS; LDA MODEL; NON-PARAMETRIC BAYESIAN;

IMAGE ENHANCEMENT;

EID: 51949085162     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2008.4587620     Document Type: Conference Paper

References (18)

1. N. Ahuja and S. Todorovic. Learning the taxonomy and models of categories present in arbitrary images. In ICCV, 2007.
2. D. Blei, A. Ng, and M. Jordan. Latent dirichlet allocation. J. Machine Learning Research, 3:993-1022, 2003.
3. D. M. Blei, T. L. Griffiths, M. I. Jordan, and J. B. Tenenbaum. Hierarchical topic models and the nested chinese restaurant process. In NIPS, 2004.
4. M. D. Escobar and M. West. Bayesian density estimation and inference using mixtures. Journal of the American Statistical Association, 90(430):577-588, 1995.
5. L. Fei-Fei and P. Perona. A bayesian hierarchical model for learning natural scene categories. In CVPR, 2005.
6. S. Gangaputra and D. Geman. A design principle for coarse-to-fine classification. In CVPR, 2006.
7. K. Grauman and T. Darrell. The pyramid match kernel: Discriminative classification with sets of image features. In ICCV, pages 1458-1465, 2005.
8. G. Griffin, A. Holub, and P. Perona. Caltech-256 object category dataset. Technical Report 7694, California Institute of Technology, 2007.
9. S. Jain and R. Neal. A split-merge markov chain monte carlo procedure for the dirichlet process mixture model. Journal of Computational and Graphical Statistics, 2000.
10. K. Kurihara, M. Welling, and N. Vlassis. Accelerated variational dp mixture models. In NIPS, 2006.
11. S. Lazebnik, C. Schmid, and J. Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In CVPR, 2006.
12. D. G. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 60(2):91-110, 2004.
13. E. Meeds. Nonparametric Bayesian methods for extracting structure from data. PhD thesis, University of Toronto, 2008.
14. T. P. Minka. Estimating a Dirichlet distribution. Technical report, MSR, 2000.
15. J. Sivic, B. C. Russell, A. A. Efros, A. Zisserman, and W. T. Freeman. Discovering object categories in image collections. Technical Report AIM-2005-005, MIT, February 2005.
16. J. Sivic, B. C. Russell, A. Zisserman, W. T. Freeman, and A. A. Efros. Unsupervised discovery of visual object class hierarchies. In CVPR, 2008.
17. A. Torralba, K. P. Murphy, and W. T. Freeman. Shared features for multiclass object detection. In Toward Category-Level Object Recognition, pages 345-361, 2006.
18. G. Wang, Y. Zhang, and L. Fei-Fei. Using dependent regions for object categorization in a generative framework. In CVPR, 2006.