Nested-SIFT for efficient image matching and retrieval

IEEE Multimedia

Volumn 20, Issue 3, 2013, Pages 34-46

  Xu, Pengfei ^a   Zhang, Lei ^b   Yang, Kuiyuan ^b   Yao, Hongxun ^a  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

^b UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

Author keywords

feature representation; image matching; image retrieval; multimedia; multimedia applications; nested SIFT; SimHash

Indexed keywords

FEATURE REPRESENTATION; MULTIMEDIA; MULTIMEDIA APPLICATIONS; NESTED-SIFT; SIMHASH;

BINARY CODES; IMAGE RETRIEVAL;

IMAGE MATCHING;

EID: 84883266835     PISSN: 1070986X     EISSN: None     Source Type: Journal    
DOI: 10.1109/MMUL.2013.18     Document Type: Article

References (14)

1. R. Szeliski, ''Image Alignment and Stitching:A Tutorial,'' Foundations and Trends in ComputerGraphics and Vision, vol. 2, no. 1, 2006,pp. 1-104.
2. S. Agarwal et al., ''Building Rome in a Day,''Proc. 12th IEEE Int'l Conf. Computer Vision, IEEE,2009, pp. 72-79.
3. N. Snavely, S. Seitz, and R. Szeliski, ''Modeling theWorld from Internet Photo Collections,'' Int'l J. ComputerVision, vol. 80, no. 2, 2008, pp. 189-210.
4. J. Philbin et al., ''Object Retrieval with LargeVocabularies and Fast Spatial Matching,'' Proc.IEEE Conf. Computer Vision and Pattern Recognition(CVPR), IEEE CS, 2007, pp. 1-8.
5. Y. Li, N. Snavely, and D. Huttenlocher, ''Location Recognition Using Prioritized Feature Matching,''Proc. European Conf. Computer Vision (ECCV),Springer-Verlag, 2010, pp. 791-804.
6. M. Charikar, ''Similarity Estimation Techniquesfrom Rounding Algorithms,'' Proc. 34th Ann.ACM Symp. Theory of Computing, ACM, 2002,pp. 380-388. (Pubitemid 35009398)
7. G. Manku, A. Jain, and A. Das Sarma, ''DetectingNear-Duplicates for Web Crawling,'' Proc. 16thInt'l Conf. World Wide Web, ACM, 2007,pp. 141-150. (Pubitemid 47582246)
8. D. Lowe, ''Distinctive Image Features From Scale-Invariant Keypoints,'' Int'l J. Computer Vision,vol. 60, no. 2, 2004, pp. 91-110.
9. J. Sivic and A. Zisserman, ''Video Google: A TextRetrieval Approach to Object Matching in Videos,''Proc. 9th IEEE Int'l Conf. Computer Vision,IEEE, 2003, pp. 1470-1477.
10. K. Mikolajczyk and C. Schmid, ''Indexing Basedon Scale Invariant Interest Points,'' Proc. 7th IEEEInt'l Conf. Computer Vision, vol. 1, IEEE, 2001,pp. 525-531. (Pubitemid 32794998)
11. Z. Wu et al., ''Bundling Features for Large Scale Partial-Duplicate Web Image Search,'' Proc. IEEEConf. Computer Vision and Pattern Recognition,IEEE CS, 2009, pp. 25-32.
12. Y. Cao et al., ''Spatial-Bag-of-Features,'' Proc. IEEEConf. Computer Vision and Pattern Recognition(CVPR), IEEE CS, 2010, pp. 3352-3359.
13. Y. Zhang, Z. Jia, and T. Chen, ''Image Retrievalwith Geometry-Preserving Visual Phrases,'' Proc.IEEE Conf. Computer Vision and Pattern Recognition(CVPR), IEEE CS, 2011, pp. 809-816.
14. H. Jegou, M. Douze, and C. Schmid, ''Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search,'' Proc. European Conf. Computer Vision (ECCV), Springer-Verlag, 2008, pp. 304-317.