Speeded-up, relaxed spatial matching

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2011, Pages 1653-1660

  Tolias, Giorgos ^a   Avrithis, Yannis ^a  

^a NATIONAL TECHNICAL UNIVERSITY OF ATHENS   (Greece)

Author keywords

[No Author keywords available]

Indexed keywords

DESCRIPTORS; FEATURE CORRESPONDENCE; GEOMETRIC INVARIANCE; MATCHING PROCESS; MULTIPLE MATCHING; NON-RIGID OBJECTS; ONE-TO-ONE MAPPINGS; RE-RANKING; RIGIDITY CONSTRAINT; SPACE REQUIREMENTS; SPATIAL MATCHING; STATE OF THE ART;

IMAGE RETRIEVAL; SEARCH ENGINES;

IMAGE PROCESSING;

EID: 84856656228     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2011.6126427     Document Type: Conference Paper

References (19)

1. Y. Avrithis, Y. Kalantidis, G. Tolias, and E. Spyrou. Retrieving landmark and non-landmark images from community photo collections. In ACM Multimedia, Firenze, Italy, October 2010. 8
2. Y. Avrithis, G. Tolias, and Y. Kalantidis. Feature map hashing: Sub-linear indexing of appearance and global geometry. In ACM Multimedia, Firenze, Italy, October 2010. 8
3. D. Ballard. Generalizing the hough transform to detect arbitrary shapes. Pattern Recognition, 1981. 2
4. H. Bay, T. Tuytelaars, and L. Van Gool. SURF: Speeded up robust features. In ECCV, 2006. 6
5. G. Carneiro and A. Jepson. Flexible spatial configuration of local image features. PAMI, pages 2089-2104, 2007. 2, 4
6. O. Chum, J. Philbin, J. Sivic, M. Isard, and A. Zisserman. Total recall: Automatic query expansion with a generative feature model for object retrieval. In ICCV, 2007. 8
7. M. Fischler and R. Bolles. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6):381-395, 1981. 2
8. K. Grauman and T. Darrell. The pyramid match kernel: Efficient learning with sets of features. Journal of Machine Learning Research, 8:725-760, 2007. 1, 2, 3
9. H. Jégou, M. Douze, and C. Schmid. Improving bag-offeatures for large scale image search. IJCV, 87(3):316-336, 2010. 2, 5, 6, 8
10. S. Lazebnik, C. Schmid, and J. Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In CVPR, volume 2, page 1, 2006. 2
11. M. Leordeanu and M. Hebert. A spectral technique for correspondence problems using pairwise constraints. In ICCV, volume 2, pages 1482-1489, 2005. 1, 2, 4
12. D. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 60(2):91-110, 2004. 1, 2
13. M. Perdoch, O. Chum, and J. Matas. Efficient representation of local geometry for large scale object retrieval. In CVPR, 2009. 7
14. J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman. Object retrieval with large vocabularies and fast spatial matching. In CVPR, 2007. 1, 2, 6, 7
15. J. Philbin, O. Chum, J. Sivic, M. Isard, and A. Zisserman. Lost in quantization: Improving particular object retrieval in large scale image databases. In CVPR, 2008. 2, 6, 7, 8
16. J. Sivic and A. Zisserman. Video Google: A text retrieval approach to object matching in videos. In ICCV, pages 1470-1477, 2003. 5, 6
17. A. Vedaldi and S. Soatto. Quick shift and kernel methods for mode seeking. In ECCV, 2008. 2
18. A. Vedaldi and S. Soatto. Relaxed matching kernels for robust image comparison. In CVPR, 2008. 2
19. Z. Wu, Q. Ke, M. Isard, and J. Sun. Bundling features for large scale partial-duplicate web image search. In CVPR,