Large-scale image retrieval based on boosting iterative quantization hashing with query-adaptive reranking

Neurocomputing

Volumn 122, Issue , 2013, Pages 480-489

  Fu, Haiyan ^a   Kong, Xiangwei ^a   Lu, Jiayin ^a  

^a DALIAN UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Boosting iterative quantization hashing; Image hashing; Large scale retrieval; Query adaptive reranking

Indexed keywords

APPROXIMATE NEAREST NEIGHBORS (ANN); BOOSTING ITERATIVE QUANTIZATION HASHING; HASHING FUNCTIONS; IMAGE HASHING; LARGE-SCALE RETRIEVAL; RE-RANKING; SEARCH PERFORMANCE; STATE-OF-THE-ART METHODS;

HAMMING DISTANCE; IMAGE RETRIEVAL;

ITERATIVE METHODS;

ACCURACY; ARTICLE; BOOSTING ITERATIVE QUANTIZATION HASHING; CONTROLLED STUDY; IMAGE RETRIEVAL; INTERMETHOD COMPARISON; MACHINE LEARNING; PRIORITY JOURNAL; PROCESS OPTIMIZATION; QUERY ADAPTIVE RERANKING;

EID: 84884208265     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2013.05.033     Document Type: Article

References (27)

1. J. Brandt, Transform coding for fast approximate nearest neighbor search in high dimensions, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, USA, 2010, pp. 1815-1822.
2. Liu D. A strong lower bound for approximate nearest neighbor searching. Inf. Process. Lett. 2004, 92(1):23-29.
3. Indyk P. Nearest-neighbor-preserving embeddings. ACM Trans. Algorithms 2007, 3(3):1549-6325.
4. Jégou H., Douze M., Schmid C. Improving bag-of-features for large scale image search. Int. J. Comput. Vision 2010, 87(3):316-336.
5. W. Liu, J. Wang, S. Kumar, S. Chang, Hashing with graphs, in: Proceedings of International Conference on Machine Learning, Bellevue, Washington, USA, 2011.
6. Andoni A., Indyk P. Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. Commun. ACM 2008, 51(1):117-122.
7. B. Kulis, K. Grauman, Kernelized locality-sensitive hashing for scalable image search, in: Proceedings of IEEE International Conference on Computer Vision, Kyoto, Japan, 2009, pp. 2130-2137.
8. M. Raginsky, S. Lazebnik, Locality-sensitive binary codes from shift-invariant kernels, in: Proceedings of Annual Conference on Neural Information Processing Systems, Vancouver, Canada, 2009, pp. 1509-1517.
9. A. Torralba, R. Fergus, Y. Weiss, Small codes and large image databases for recognition, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Alaska, USA, 2008, pp. 1-8.
10. Y. Weiss, A. Torralba, R. Fergus, Spectral hashing, in: Proceedings of Annual Conference on Neural Information Processing Systems, Vancouver, Canada, 2008, pp. 1753-1760.
11. J. Wang, S. Kumar, S. Chang, Semi-supervised hashing for scalable image retrieval, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, USA, 2010, pp. 3424-3431.
12. Y. Mu, X. Chen, T. Chua, S. Yan, Learning reconfigurable hashing for diverse semantics, in: Proceedings of ACM International Conference on Multimedia Retrieval, Trento, Italy, 2011, pp. 1-8.
13. Strecha C., Bronstein A.M., Bronstein M.M., Fua P. LDAHash. improved matching with smaller descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 2012, 34(1):66-78.
14. Yao C., Bu J., Wu C., Chen G. Semi-supervised spectral hashing for fast similarity search. Neurocomputing 2013, 101:52-58.
15. Shao J., Wu F., Ouyang C., Zhang X. Sparse spectral hashing. Pattern Recognition Lett. 2011, 33(3):271-277.
16. J. Zhou, H. Fu, X. Kong, A balanced semi-supervised hashing method for CBIR, in: Proceedings of IEEE International Conference on Image Processing, Brussels, Belgium, 2011, pp. 2481-2484.
17. Y. Gong, S. Lazebnik, Iterative quantization: a procrustean approach to learning binary codes, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, USA, 2011, pp. 817-824.
18. H. Xu, J. Wang, Z. Li, G. Zeng, S. Li, N. Yu, Complementary hashing for approximate nearest neighbor search, in: Proceedings of IEEE International Conference on Computer Vision, Barcelona, Spain, 2011, pp. 1631-1638.
19. J. Wang, S. Kumar, S. Chang, Sequential projection learning for hashing with compact codes, in: Proceedings of International Conference on Machine Learning, Haifa, Israel, 2010, pp. 1127-1134.
20. G. Shakhnarovich, P. Viola, T. Darrell, Fast pose estimation with parameter-sensitive hashing, in: Proceedings of IEEE International Conference on Computer Vision, Beijing, China, 2003, pp. 750-757.
21. X. Wang, L. Zhang, F. Jing, W. Ma, Annosearch: image auto-annotation by search, in: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, New York, USA, 2006, pp. 1483-1490.
22. H. Jégou, M. Douze, C. Schmid, Hamming embedding and weak geometric consistency for large scale image search, in: Proceedings of European Conference on Computer Vision, Marseille, France, 2008, pp. 304-317.
23. H. Jégou, M. Douze, C. Schmid, P. Pérez, Aggregating local descriptors into a compact image representation, in: Proceedings of Computer Vision and Pattern Recognition, San Francisco, USA, 2010, pp. 3304-3311.
24. J. Wang, Semi-Supervised Learning for Scalable and Robust Visual Search, Ph.D. Thesis, Columbia University, 2011.
25. Friedman J., Hastie T., Tibshirani R. Additive logistic regression. a statistical view of boosting. Ann. Stat. 2000, 28(2):337-407.
26. Oliva A., Torralba A. Modeling the shape of the scene. a holistic representation of the spatial envelope. Int. J. Comput. Vision 2001, 42(3):145-175.
27. Xing Xie, Lie Lu, Menglei Jia, Hua Li, Frank Seide, Wei-Ying Ma, Mobile search with multimodal queries, in: Proceedings of the IEEE, 96 (4), 2008.