| Robust wide-baseline stereo from maximally stable extremal regions J Matas, O Chum, M Urban, T Pajdla Image and vision computing 22 (10), 761-767, 2004 | 5582 | 2004 |
| Object retrieval with large vocabularies and fast spatial matching J Philbin, O Chum, M Isard, J Sivic, A Zisserman 2007 IEEE conference on computer vision and pattern recognition, 1-8, 2007 | 3036 | 2007 |
| Lost in quantization: Improving particular object retrieval in large scale image databases J Philbin, O Chum, M Isard, J Sivic, A Zisserman 2008 IEEE conference on computer vision and pattern recognition, 1-8, 2008 | 1555 | 2008 |
| Matching with PROSAC-progressive sample consensus O Chum, J Matas 2005 IEEE computer society conference on computer vision and pattern …, 2005 | 1110 | 2005 |
| Total recall: Automatic query expansion with a generative feature model for object retrieval O Chum, J Philbin, J Sivic, M Isard, A Zisserman 2007 IEEE 11th International Conference on Computer Vision, 1-8, 2007 | 906 | 2007 |
| Locally optimized RANSAC O Chum, J Matas, J Kittler Joint Pattern Recognition Symposium, 236-243, 2003 | 679 | 2003 |
| Near Duplicate Image Detection: min-Hash and tf-idf Weighting. O Chum, J Philbin, A Zisserman BMVC 810, 812-815, 2008 | 529 | 2008 |
| CNN image retrieval learns from BoW: Unsupervised fine-tuning with hard examples F Radenović, G Tolias, O Chum European conference on computer vision, 3-20, 2016 | 438 | 2016 |
| Optimal randomized RANSAC O Chum, J Matas IEEE Transactions on Pattern Analysis and Machine Intelligence 30 (8), 1472-1482, 2008 | 396 | 2008 |
| Negative evidences and co-occurences in image retrieval: The benefit of PCA and whitening H Jégou, O Chum European conference on computer vision, 774-787, 2012 | 356 | 2012 |
| An exemplar model for learning object classes O Chum, A Zisserman 2007 IEEE Conference on Computer Vision and Pattern Recognition, 1-8, 2007 | 350 | 2007 |
| USAC: a universal framework for random sample consensus R Raguram, O Chum, M Pollefeys, J Matas, JM Frahm IEEE transactions on pattern analysis and machine intelligence 35 (8), 2022-2038, 2012 | 325 | 2012 |
| Geometric min-hashing: Finding a (thick) needle in a haystack O Chum, M Perd'och, J Matas 2009 IEEE Conference on Computer Vision and Pattern Recognition, 17-24, 2009 | 319 | 2009 |
| Efficient representation of local geometry for large scale object retrieval M Perd'och, O Chum, J Matas 2009 IEEE Conference on Computer Vision and Pattern Recognition, 9-16, 2009 | 305 | 2009 |
| Total recall II: Query expansion revisited O Chum, A Mikulik, M Perdoch, J Matas CVPR 2011, 889-896, 2011 | 296 | 2011 |
| Scalable near identical image and shot detection O Chum, J Philbin, M Isard, A Zisserman Proceedings of the 6th ACM international conference on Image and video …, 2007 | 282 | 2007 |
| Fine-tuning CNN image retrieval with no human annotation F Radenović, G Tolias, O Chum IEEE transactions on pattern analysis and machine intelligence 41 (7), 1655-1668, 2018 | 281 | 2018 |
| Randomized RANSAC with Td, d test O Chum, J Matas Proc. British Machine Vision Conference 2, 448-457, 2002 | 213 | 2002 |
| Randomized RANSAC with Td, d test J Matas, O Chum Image and vision computing 22 (10), 837-842, 2004 | 187 | 2004 |
| Learning a fine vocabulary A Mikulík, M Perdoch, O Chum, J Matas European conference on computer vision, 1-14, 2010 | 173 | 2010 |
