Better matching with fewer features: The selection of useful features in large database recognition problems

2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009

Volumn , Issue , 2009, Pages 2109-2116

  Turcot, Panu ^a   Lowe, David G ^a  

^a UNIVERSITY OF BRITISH COLUMBIA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

BAG OF WORDS; FEATURE SETS; IMAGE FEATURES; LARGE DATABASE; LARGE DATASETS; LOCAL FEATURE; MATCHING PERFORMANCE; MEMORY REQUIREMENTS; PRE-PROCESSING STEP; RECENT PROGRESS; TRAINING FEATURES; TRAINING IMAGE; VISUAL WORD;

COMPUTER VISION;

FEATURE EXTRACTION;

EID: 77953218614     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCVW.2009.5457541     Document Type: Conference Paper

References (20)

1. http://www.graphviz.org/.
2. http://www.robots.ox.ac.uk/~vgg/data/oxbuildings/.
3. http://lear.inrialpes.fr/ jegou/data.php.
4. R. A. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1999.
5. O. Chum and J. Matas. Web scale image clustering: Large scale discovery of spatially related images. In Technical Report CTU-CMP-2008-15, Czech Technical University in Prague, 2008.
6. O. Chum, J. Matas, and J. Kittler. Locally optimized ransac. In DAGM-Symposium, pages 236-243, 2003.
7. 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 International Conference on Computer Vision, 2007.
8. H. Jegou, M. Douze, and C. Schmid. Hamming embedding and weak geometric consistency for large scale image search. In European Conference on Computer Vision, volume I of LNCS, pages 304-317, oct 2008.
9. D. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2):91-110, 2004.
10. D. G. Lowe. Local feature view clustering for 3d object recognition. In IEEE Conference on Computer Vision and Pattern Recognition, pages 682-688, 2001.
11. K. Mikolajczyk and C. Schmid. A performance evaluation of local descriptors. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 27(10):1615-1630, Oct. 2005.
12. K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir, and L. V. Gool. A comparison of affine region detectors. International Journal of Computer Vision, 65(1):43-72, 2005.
13. M. Muja and D. G. Lowe. Fast approximate nearest neighbors with automatic algorithm configuration. In International Conference on Computer Vision Theory and Applications, 2009.
14. D. Nister and H. Stewenius. Scalable recognition with a vocabulary tree. In IEEE Conference on Computer Vision and Pattern Recognition, 2006.
15. J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman. Object retrieval with large vocabularies and fast spatial matching. In IEEE Conference on Computer Vision and Pattern Recognition, 2007.
16. J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman. Lost in quantization: Improving particular object retrieval in large scale image databases. In IEEE Conference on Computer Vision and Pattern Recognition, 2008.
17. J. Philbin and A. Zisserman. Object mining using a matching graph on very large image collections. In Computer Vision, Graphics and Image Processing, Indian Conference on, 2008.
18. D. Pritchard and W. Heidrich. Cloth motion capture. In Eurographics, 2003.
19. G. Schindler, M. Brown, and R. Szeliski. City-scale location recognition. In IEEE Conference on Computer Vision and Pattern Recognition, 2007.
20. J. Sivic and A. Zisserman. Video Google: A text retrieval approach to object matching in videos. In International Conference on Computer Vision, volume 2, pages 1470-1477, Oct. 2003.