Unsupervised feature optimization (UFO): Simultaneous selection of multiple features with their detection parameters

2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009

Volumn , Issue , 2009, Pages 1263-1270

  Karlinsky, Leonid ^a   Dinerstein, Michael ^a   Ullman, Shimon ^a  

^a WEIZMANN INSTITUTE OF SCIENCE   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTER VISION; GLOBAL OPTIMIZATION;

CLASSIFICATION PERFORMANCE; DETECTION THRESHOLD; FEATURE CONSISTENCY; FEATURE OPTIMIZATIONS; MULTIPLE CLASSIFICATION; MUTUAL INFORMATIONS; OPTIMIZATION TECHNIQUES; PARAMETRIC MODELING;

FEATURE EXTRACTION;

EID: 70450195646     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPRW.2009.5206499     Document Type: Conference Paper

References (23)

1. S. Agarwal, A. Awan, and D. Roth. Learning to detect objects in images via a sparse, part-based representation. PAMI, 2004.
2. N. Ahuja and S. Todorovic. Discovering hierarchical taxonomy of categories and shared subcategories in images. ICCV, 2007.
3. E. Borenstein and S. Ullman. Class-specific, top-down segmentation. ECCV, 2002.
4. L. Cao and L. Fei-Fei. Spatially coherent latent topic model for concurrent object segmentation and classification. ICCV, 2007.
5. R. Fergus, L. Fei-Fei, P. Perona, and A. Zisserman. Learning object categories from google's image search. ICCV, pages 1816-1823, 2005. (Pubitemid 44042397)
6. R. Fergus, P. Perona, and A. Zisserman. Object class recognition by unsupervised scale-invariant learning. CVPR (2), pages 264-271, 2003.
7. R. Fergus, P. Perona, and A. Zisserman. A visual category filter for google images. ECCV, 2004.
8. M. Fritz and B. Schiele. Towards unsupervised discovery of visual categories. DAGM, 2006.
9. F. Jurie and B. Triggs. Creating efficient codebooks for visual recognition. ICCV, 2005.
10. L. Karlinsky, M. Dinerstein, D. Levi, and S. Ullman. Unsupervised classification and part localization by consistency amplification. ECCV, 2008.
11. B. Leibe, A. Leonardis, and B. Schiele. Combined object categorization and segmentation with an implicit shape model. ECCV, 2004.
12. L.-J. Li, G.Wang, and L. Fei-Fei. Optimol: automatic object picture collection via incremental model learning. CVPR, 2007.
13. D. Liu and T. Chen. Semantic-shift for unsupervised object detection. CVPR Workshop, 2006.
14. D. Liu and T.Chen. Unsupervised image categorization and object localization using topic models and correspondences between images. ICCV, 2007.
15. N. Loeff, H. Arora, A.Sorokin, and D. Forsyth. Efficient unsupervised learning for localization and detection in object categories. NIPS, 2005.
16. S. Marcel. Hand posture recognition in a body-face centered space. Conference on Human Factors in Computer Systems (CHI), 1999.
17. S. Nowozin, K. Tsuda, T. Uno, T. Kudo, and G. H. Bakir. Weighted substructure mining for image analysis. CVPR, 2007.
18. T. Quack, V. Ferrari, B. Leibe, and L. V. Gool. Efficient mining of frequent and distinctive feature configurations. ICCV, 2007.
19. B. C. Russell, W. T. Freeman, A. A. Efros, J. Sivic, and A. Zisserman. Using multiple segmentations to discover objects and their extent in image collections. CVPR, 2006.
20. J. Sivic, B. C. Russell, A. A. Efros, A. Zisserman, and W. T. Freeman. Discovering objects and their localization in images. ICCV, pages 370-377, 2005.
21. M. Varma and D. Ray. Learning the discriminative powerinvariance trade-off. ICCV, 2007.
22. Y.Weiss. Segmentation using eigenvectors: A unifying view. ICCV, 1999.
23. L. Zhu, C. Lin, H. Huang, Y. Chen, and A. Yuille. Unsupervised structure learning: Hierarchical recursive composition, suspicious coincidence and competitive exclusion. ECCV, 2008.