Dense interest points

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2010, Pages 2281-2288

  Tuytelaars, Tinne ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

APPLICATION DOMAINS; FEATURE DETECTOR; FIXED SIZE; HYBRID SCHEME; ILLUMINATION INVARIANCE; IMAGE PATCHES; IMAGE REPRESENTATIONS; INTEREST POINTS; LOCAL FEATURE; MULTIPLE SCALE; REGULAR GRIDS; SCALE PARAMETER; SPATIAL RELATIONS; STANDARD TOOLS;

COMPUTER VISION;

EID: 77956000050     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2010.5539911     Document Type: Conference Paper

References (23)

1. M. Barnard and K. Mikolajczyk. Feature detectors and descriptors: The state of the art and beyond, evaluation. http://www.featurespace.org/.
2. M. Brown, R. Szeliski, and S. Winder. Multi-image matching using multi-scale oriented patches. In CVPR, pages 510-517, 2005.
3. C.-C. Chang and C.-J. Lin. LIBSVM: a library for support vector machines, 2001. http://www.csie.ntu.edu.tw/ cjlin/libsvm.
4. B. T. E. Nowak, F. Jurie. Sampling strategies for bag-of-features image classification. In ECCV, 2006.
5. M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results.
6. L. Fei-Fei and P. Perona. A bayesian hierarchical model for learning natural scene categories. In CVPR, 2005.
7. R. Fergus, P. Perona, and A. Zisserman. Weakly supervised scale-invariant learning of models for visual recognition. IJCV, 71(3):273-303, 2007.
8. C. S. K.E.A. van de Sande, T. Gevers. A comparison of color features for visual concept classification. In ACM Conference on Image and Video Retrieval, 2008.
9. B. Leibe, A. Leonardis, and B.Schiele. Robust object detection with interleaved categorization and segmentation. IJCV, 77(1-3):259-289, 2008.
10. C. Liu, J. Yuen, and A. Torralba. Nonparametric scene parsing: label transfer via dense scene alignment. In CVPR, 2009.
11. C. Liu, J. Yuen, A. Torralba, J. Sivic, and W. T. Freeman. Sift flow: dense correspondence across different scenes. In ECCV, 2008. http://people.csail.mit.edu/celiu/ECCV2008/.
12. D. Lowe. Distinctive image features from scale-invariant keypoint. IJCV, 2(60):91-110, 2004.
13. R. Marée, P. Geurts, J. Piater, and L. Wehenkel. Random sub-windows for robust image classification. In CVPR, 2005.
14. K. Mikolajczyk and C. Schmid. Scale and affine invariant interest point detectors. IJCV, 1(60):63-86, 2004.
15. K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir, and L. Van Gool. A comparison of affine region detectors. IJCV, 65(1-2):43-72, 2005.
16. M. Pollefeys. Automatic 3D Modeling with a Hand-Held Camera Images, 2004. ISPRS 2004/3DPVT 2004 tutorial.
17. X. Ren and J. Malik. Learning a classification model for segmentation. IJCV, 1:10-17, 2003.
18. J. Sivic, F. Schaffalitzky, and A. Zisserman. Object level grouping for video shots. IJCV, 67(2):189-210, 2006.
19. J. Sivic and A. Zisserman. Video Google: A text retrieval approach to object matching in videos. In ICCV, 2003.
20. E. Tola, V. Lepetit, and P. Fua. A fast local descriptor for dense matching. In CVPR, 2008.
21. T.Quack, V. Ferrari, B. Leibe, and L. V. Gool. Efficient mining of frequent and distinctive feature configurations. In ICCV, 2007.
22. T. Tuytelaars and K. Mikolajczyk. Local invariant feature detectors: A survey. Foundations and Trends in Computer Graphics and Vision, 3(3):177-280, 2008.
23. T. Tuytelaars and C. Schmid. Vector quantizing feature space with a regular lattice. In ICCV, 2007.