Large-scale unsupervised hashing with shared structure learning

IEEE Transactions on Cybernetics

Volumn 45, Issue 9, 2015, Pages 1811-1822

  Liu, Xianglong ^a   Mu, Yadong ^b   Zhang, Danchen ^a   Lang, Bo ^a   Li, Xuelong ^c  

^a BEIHANG UNIVERSITY   (China)

^b AT AND T LABS RESEARCH   (United States)

^c XI AN INSTITUTE OF OPTICS AND PRECISION MECHANICS   (China)

Author keywords

Locality sensitive hashing (LSH); nearest neighbor search; shared structure learning; unsupervised hashing.

Indexed keywords

CONTENT BASED RETRIEVAL; HASH FUNCTIONS; IMAGE RETRIEVAL; LEARNING ALGORITHMS; NEAREST NEIGHBOR SEARCH;

ALTERNATING OPTIMIZATIONS; CLUSTER DISTRIBUTIONS; COMPLEMENTARY SUBSPACES; LOCAL NEIGHBORHOOD STRUCTURES; LOCALITY SENSITIVE HASHING; SEQUENTIAL LEARNING ALGORITHM; SHARED STRUCTURES; UNSUPERVISED HASHING;

ORTHOGONAL FUNCTIONS;

EID: 85027950483     PISSN: 21682267     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCYB.2014.2360856     Document Type: Article

References (53)

1. J. L. Bentley, "Multidimensional binary search trees used for associative searching, " Commun. ACM, vol. 18, no. 9, pp. 509-517, 1975
2. J. He et al., "Mobile product search with bag of hash bits and boundary reranking, " in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2012, pp. 3005-3012
3. S. Xiang, J. Yang, and J. Huang, "Perceptual video hashing robust against geometric distortions, " Sci. China Inf. Sci., vol. 55, no. 7, pp. 1520-1527, 2012
4. L. Chen, D. Xu, I.-H. Tsang, and X. Li, "Spectral embedded hashing for scalable image retrieval, " IEEE Trans. Cybern., vol. 44, no. 7, pp. 1180-1190, Jul. 2014
5. X. Nie, J. Liu, J. Sun, L. Wang, and X. Yang, "Robust video hashing based on representative-dispersive frames, " Sci. China Inf. Sci., vol. 56, no. 6, pp. 1-11, 2013
6. J. Song, Y. Yang, X. Li, Z. Huang, and Y. Yang, "Robust hashing with local models for approximate similarity search, " IEEE Trans. Cybern., vol. 44, no. 7, pp. 1225-1236, Jul. 2014
7. D. G. Lowe, "Distinctive image features from scale-invariant keypoints, " Int. J. Comput. Vis., vol. 60, no. 2, pp. 91-110, Nov. 2004
8. A. Oliva and A. Torralba, "Modeling the shape of the scene: A holistic representation of the spatial envelope, " Int. J. Comput. Vis., vol. 42, no. 3, pp. 145-175, 2001
9. S. Arya, D. M. Mount, N. S. Netanyahu, R. Silverman, and A. Y. Wu, "An optimal algorithm for approximate nearest neighbor searching fixed dimensions, " J. ACM, vol. 45, no. 6, pp. 891-923, 1998
10. T. Liu, A. W. Moore, A. G. Gray, and K. Yang, "An investigation of practical approximate nearest neighbor algorithms, " in Proc. Adv. Neural Inf. Process. Syst. (NIPS), 2004, pp. 825-832
11. J. E. Goodman, J. O'Rourke, and P. Indyk, Chapter 39: Nearest Neighbours in High-Dimensional Spaces, Handbook of Discrete and Computational Geometry, 2nd ed. Boca Raton, FL, USA: CRC Press, 2004
12. P. Indyk and R. Motwani, "Approximate nearest neighbors: Towards removing the curse of dimensionality, " in Proc. 30th Annu. ACM Symp. Theory Comput. (STOC), 1998, pp. 604-613
13. M. Datar, N. Immorlica, P. Indyk, and V. S. Mirrokni, "Locality-sensitive hashing scheme based on p-stable distributions, " in Proc. 20th Annu. Symp. Comput. Geom. (SCG), Brooklyn, NY, USA, 2004, pp. 253-262
14. Y. Weiss, A. Torralba, and R. Fergus, "Spectral hashing, " in Proc. Adv. Neural Inf. Process. Syst. (NIPS), 2008, pp. 1-8
15. B. Kulis and T. Darrell, "Learning to hash with binary reconstructive embeddings, " in Proc. Adv. Neural Inf. Process. Syst. (NIPS), 2009, pp. 1042-1050
16. B. Kulis and K. Grauman, "Kernelized locality-sensitive hashing for scalable image search, " in Proc. Int. Conf. Comput. Vis. (ICCV), Kyoto, Japan, 2009, pp. 2130-2137
17. J. Wang, S. Kumar, and S.-F. Chang, "Sequential projection learning for hashing with compact codes, " in Proc. 27th Int. Conf. Mach. Learn., 2010, pp. 1127-1134
18. Y. Mu, J. Shen, and S. Yan, "Weakly-supervised hashing in kernel space, " in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), San Francisco, CA, USA, 2010, pp. 3344-3351
19. J. Wang, S. Kumar, and S.-F. Chang, "Semi-supervised hashing for largescale search, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 12, pp. 2393-2406, Dec. 2012
20. Y. Gong, S. Lazebnik, A. Gordo, and F. Perronnin, "Iterative quantization: A procrustean approach to learning binary codes for large-scale image retrieval, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 12, pp. 2916-2929, Dec. 2013
21. M. Norouzi and D. J. Fleet, "Cartesian K-Means, " in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2013, pp. 3017-3024
22. K. He, F. Wen, and J. Sun, "K-Means hashing: An affinity-preserving quantization method for learning binary compact codes, " in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2013, pp. 2938-2945
23. A. Gordo, F. Perronnin, Y. Gong, and S. Lazebnik, "Asymmetric distances for binary embeddings, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 36, no. 1, pp. 33-47, Jan. 2014
24. M. S. Charikar, "Similarity estimation techniques from rounding algorithms, " in Proc. 34th Annu. ACM Symp. Theory Comput., 2002, pp. 380-388
25. Y. Gong and S. Lazebnik, "Iterative quantization: A procrustean approach to learning binary codes, " in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2011, pp. 817-824
26. W. Liu, J. Wang, S. Kumar, and S.-F. Chang, "Hashing with graphs, " in Proc. 28th Int. Conf. Mach. Learn. (ICML), Bellevue, WA, USA, 2011, pp. 1-8
27. X. Liu, J. He, D. Liu, and B. Lang, "Compact kernel hashing with multiple features, " in Proc. 20th ACM Int. Conf. Multimedia (ACM MM), 2012, pp. 881-884
28. X. Liu, J. He, C. Deng, and B. Lang, "Collaborative hashing, " in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2014, pp. 2147-2154
29. J. Wang, S. Kumar, and S.-F. Chang, "Semi-supervised hashing for scalable image retrieval, " in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), San Francisco, CA, USA, 2010, pp. 3424-3431
30. X. Liu, Y. Mu, B. Lang, and S.-F. Chang, "Compact hashing for mixed image-keyword query over multi-label images, " in Proc. 2nd ACM Int. Conf. Multimedia Retrieval (ACM ICMR), 2012, pp. 18:1-18:8
31. Y. Lin, R. Jin, D. Cai, S. Yan, and X. Li, "Compressed hashing, " in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2013, pp. 446-451
32. X. Liu, J. He, B. Lang, and S.-F. Chang, "Hash bit selection: A unified solution for selection problems in hashing, " in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Portland, OR, USA, 2013, pp. 1570-1577
33. Z. Jin et al., "Complementary projection hashing, " in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), Sydney, NSW, Australia, Dec. 2013, pp. 257-264
34. C. Deng, R. Ji, W. Liu, X. Gao, and D. Tao, "Visual reranking through weakly supervised multi-graph learning, " IEEE Trans. Multimedia, vol. 16, no. 3, pp. 785-795, Apr. 2014
35. R. K. Ando and T. Zhang, "A framework for learning predictive structures from multiple tasks and unlabeled data, " J. Mach. Learn. Res., vol. 6, pp. 1817-1853, Dec. 2005
36. S. Ji, L. Tang, S. Yu, and J. Ye, "Extracting shared subspace for multilabel classification, " in Proc. 14th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., 2008, pp. 381-389
37. Z. Kang, K. Grauman, and F. Sha, "Learning with whom to share in multi-task feature learning, " in Proc. 28th Int. Conf. Mach. Learn. (ICML), 2011, pp. 521-528
38. B. Chen, W. Lam, I. Tsang, and T.-L. Wong, "Extracting discriminative concepts for domain adaptation in text mining, " in Proc. 15th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., 2009, pp. 179-188
39. A. Broder, M. Charikar, A. Frieze, and M. Mitzenmacher, "Min-wise independent permutations, " in Proc. 30th Annu. ACM Symp. Theory Comput. (STOC), 1998, pp. 327-336
40. J. He, W. Liu, and S.-F. Chang, "Scalable similarity search with optimized kernel hashing, " in Proc. 16th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., 2010, pp. 1129-1138
41. D. Zhang, F. Wang, and L. Si, "Composite hashing with multiple information sources, " in Proc. 34th Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2011, pp. 225-234
42. M. Turk and A. Pentland, "Eigenfaces for recognition, " J. Cognit. Neurosci., vol. 3, no. 1, pp. 71-86, Jan. 1991
43. D. L. Swets and J. J. Weng, "Using discriminant eigenfeatures for image retrieval, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 18, no. 8, pp. 831-836, Aug. 1996
44. S. Roweis and L. Saul, "Nonlinear dimensionality reduction by locally linear embedding, " Science, vol. 290, no. 5500, pp. 2323-2326, Dec. 2000
45. A. Argyriou, T. Evgeniou, and M. Pontil, "Convex multi-task feature learning, " Mach. Learn., vol. 73, no. 3, pp. 243-272, Dec. 2008
46. N. J. Higham, Accuracy and Stability of Numerical Algorithms, 2nd ed. Philadelphia, PA, USA: SIAM, 2002
47. J. Song, Y. Yang, Z. Huang, H. T. Shen, and R. Hong, "Multiple feature hashing for real-time large scale near-duplicate video retrieval, " in Proc. 19th ACM Int. Conf. Multimedia (MM), 2011, pp. 423-432
48. D. C. Sorensen, "Implicitly restarted arnoldi/lanczos methods for large scale eigenvalue calculations, " Dept. Comput. Appl. Math., Rice Univ., Houston, TX, USA, Tech. Rep. TR-96-40, 1996
49. A. Krizhevsky, "Learning multiple layers of features from tiny images, " Tech. Rep., 2009
50. T.-S. Chua et al., "NUS-WIDE: A real-world web image database from National University of Singapore, " in Proc. ACM Int. Conf. Image Video Retrieval (CIVR), 2009, pp. 48:1-48:9
51. F. Yu, R. Ji, M.-H. Tsai, G. Ye, and S.-F. Chang, "Weak attributes for large-scale image retrieval, " in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2012, pp. 2949-2956
52. S. Lazebnik, C. Schmid, and J. Ponce, "Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories, " in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2006, pp. 2169-2178
53. W. Liu, J. Wang, R. Ji, Y.-G. Jiang, and S.-F. Chang, "Supervised hashing with kernels, " in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Providence, RI, USA, 2012, pp. 2047-2081