Scalable mining of small visual objects

MM 2012 - Proceedings of the 20th ACM International Conference on Multimedia

Volumn , Issue , 2012, Pages 599-608

  Letessier, Pierre ^a,b   Buisson, Olivier ^a   Joly, Alexis ^b  

^a INA   (France)

^b INRIA   (France)

Author keywords

computer vision; discovery; hashing; LSH; mining; RMMH; scalable; small objects; weak geometry

Indexed keywords

DISCOVERY; HASHING; LSH; RMMH; SCALABLE; SMALL OBJECTS;

COMPUTER VISION; MINING; PROBABILITY DENSITY FUNCTION; SCALABILITY;

GEOMETRY;

EID: 84871368063     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2393347.2393431     Document Type: Conference Paper

References (20)

1. O. Chum, A. Mikulik, M. Perdoch, and J. Matas. Total recall ii: Query expansion revisited. In CVPR, pages 889-896, Colorado Springs, USA, 2011. IEEE.
2. O. Chum, M. Perdoch, and J. Matas. Geometric min-hashing: Finding a (thick) needle in a haystack. In CVPR, pages 17-24. IEEE, June 2009.
3. 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. IEEE, October 2007.
4. M. Datar and P. Indyk. Locality-sensitive hashing scheme based on p-stable distributions. In SCG, pages 253-262. ACM Press, 2004.
5. M. A. Fischler and R. C. Bolles. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM, 24(6):381-395, 1981.
6. H. Jégou, M. Douze, and C. Schmid. Hamming Embedding and Weak Geometry Consistency for Large Scale Image Search. Research Report RR-6709, INRIA, 2008.
7. A. Joly and O. Buisson. A Posteriori Multi-Probe Locality Sensitive Hashing. In ACM Multimedia, pages 209-218, Vancouver, Canada, oct 2008.
8. A. Joly and O. Buisson. Logo retrieval with a contrario visual query expansion. In ACM Multimedia, pages 581-584, Beijing, China, october 2009.
9. A. Joly and O. Buisson. Random Maximum Margin Hashing. In CVPR, Colorado Springs, 2011. IEEE.
10. Y. Kalantidis, L. Pueyo, M. Trevisiol, R. van Zwol, and Y. Avrithis. Scalable triangulation-based logo recognition. In ICMR, pages 20:1-20:7, Trento, Italy, 2011. ACM.
11. P. Letessier, O. Buisson, and A. Joly. Consistent visual words mining with adaptive sampling. In ICMR, pages 49:1-49:8. ACM, april 2011.
12. K. Ling and G. Wu. Frequency based locality sensitive hashing. In ICMT, pages 4929-4932, july 2011.
13. D. Lowe. Object recognition from local scale-invariant features. In ICCV, page 1150, Kerkyra, 1999. IEEE.
14. F. Olken. Random Sampling from Databases. PhD thesis, University of California, 1993.
15. J. Philbin. Scalable Object Retrieval in Very Large Image Collections. PhD thesis, Univ. of Oxford, 2010.
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 CVPR, pages 1-8, Anchorage, USA, june 2008. IEEE.
17. G. F. Pineda, H. Koga, and T. Watanabe. Object discovery by clustering correlated visual word sets. In ICPR, pages 750-753, Washington, USA, 2010. IEEE.
18. T. Tuytelaars, C. H. Lampert, M. B. Blaschko, and W. Buntine. Unsupervised object discovery: A comparison. IJCV, 88:284-302, June 2010.
19. T. Weyand and B. Leibe. Discovering favorite views of popular places with iconoid shift. In ICCV, pages 1132-1139. IEEE, Nov. 2011.
20. W. Zhao, X. Wu, and C.-W. Ngo. On the annotation of web videos by efficient near-duplicate search. IEEE Transactions on Multimedia, 12(5):448-461, 2010.