Hash functions for near duplicate image retrieval

2009 Workshop on Applications of Computer Vision, WACV 2009

Volumn , Issue , 2009, Pages

  Auclair, Adrien ^a   Vincent, Nicole ^a   Cohen, Laurent D ^b  

^a Huawei Technologies Co   (France)

^b UNIVERSITÉ PARIS DAUPHINE   (France)

Author keywords

[No Author keywords available]

Indexed keywords

BAG OF WORDS; DATA SETS; K-MEANS; LOCAL IMAGE DESCRIPTORS; NEAR-DUPLICATE IMAGE RETRIEVAL; OFF-LINE LEARNING; STATE-OF-THE-ART ALGORITHMS;

COMPUTER APPLICATIONS; COMPUTER VISION; HASH FUNCTIONS; TECHNICAL PRESENTATIONS;

IMAGE RETRIEVAL;

EID: 77951203314     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WACV.2009.5403104     Document Type: Conference Paper

References (16)

1. http://www.math-info.univ-paris5.fr/auclair/dataset.html.
2. A. Auclair, L. D. Cohen, and N. Vincent. How to use SIFT vectors to analyze an image with database templates. In Proceedings of the 5th International Workshop on Adaptive Multimedia Retrieval (AMR), Paris, France, July 2007.
3. M. Datar, N. Immorlica, P. Indyk, and V. S. Mirrokni. Locality-sensitive hashing scheme based on p-stable distributions. In SCG '04: Proceedings of the twentieth annual symposium on Computational geometry, pages 253-262, New York, NY, USA, 2004. ACM.
4. J. J. Foo and R. Sinha. Pruning sift for scalable near-duplicate image matching. In J. Bailey and A. Fekete, editors, Eighteenth Australasian Database Conference (ADC 2007), volume 63 of CRPIT, pages 63-71, Ballarat, Australia, 2007. ACS.
5. A. Gionis, P. Indyk, and R. Motwani. Similarity search in high dimensions via hashing. In The VLDB Journal, pages 518-529, 1999.
6. H. Jegou, L. Amsaleg, C. Schmid, and P. Gros. Query-adaptative locality sensitive hashing. In International Conference on Acoustics, Speech, and Signal Processing. IEEE, 2008.
7. H. Jegou, M. Douze, and C. Schmid. Hamming embedding and weak geometric consistency for large scale image search. In Proceedings of the European Conference on Computer Vision (ECCV), Marseille, France, LNCS. Springer, oct 2008.
8. Y Ke, R. Sukthankar, and L. Huston. An efficient parts-based near-duplicate and sub-image retrieval system. In MULTIMEDIA '04: Proceedings of the 12th annual ACM international conference on Multimedia, pages 869-876, New York, NY, USA, 2004. ACM Press.
9. H. Lejsek, F H. Ásmundsson, B. T. Jónsson, and L. Amsaleg. Efficient and effective image copyright enforcement. In BDA, 21èmes Journées Bases de Données Avancées, BDA 2005, Saint Malo, 17-20 octobre 2005, Actes (Informal Proceedings), 2005.
10. D. G. Lowe. Distinctive image features from scale-invariant keypoints. In International Journal of Computer Vision (IJCV), volume 20, pages 91-110, 2004.
11. D. Nistér and H. Stewénius. Scalable recognition with a vocabulary tree. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), New York, USA, pages 2161-2168, 2006.
12. J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisser-man. Object retrieval with large vocabularies and fast spatial matching. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Minneapolis, USA, 2007.
13. G. Saltan and M. McGill. Introduction to Modern Information Retrieval. McGraw-Hill, Inc., New York, NY, USA, 1986.
14. G. Shakhnarovich, T. Darrell, and P. Indyk. Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing). The MIT Press, 2006.
15. J. Sivic and A. Zisserman. Video Google: A text retrieval approach to object matching in videos. In Proceedings of the International Conference on Computer Vision (ICCV), Nice, France, volume 2, pages 1470-1477, Oct. 2003.
16. E. Valle, M. Cord, and S. Philipp-Foliguet. High-dimensional descriptor indexing for large multimedia databases. In CIKM '08: Proceeding of the 17th ACM conference on Information and knowledge management, pages 739-748, New York, NY, USA, 2008. ACM.