Weighted component hashing of binary aggregated descriptors for fast visual search

IEEE Transactions on Multimedia

Volumn 17, Issue 6, 2015, Pages 828-842

  Duan, Ling Yu ^a   Lin, Jie ^a   Wang, Zhe ^a   Huang, Tiejun ^a   Gao, Wen ^a  

^a PEKING UNIVERSITY   (China)

Author keywords

Aggregating local features; hamming space; hashing; visual search

Indexed keywords

HAMMING DISTANCE; IMAGE RETRIEVAL; MOTION PICTURE EXPERTS GROUP STANDARDS; QUERY PROCESSING; SCALES (WEIGHING INSTRUMENTS);

COMPUTATIONAL COSTS; HAMMING SPACE; HASHING; LOCAL FEATURE; MOBILE VISUAL SEARCHES; SPECIFIC COMPONENT; VECTOR OF LOCALLY AGGREGATED DESCRIPTORS; VISUAL SEARCH;

AGGREGATES;

EID: 84929304812     PISSN: 15209210     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMM.2015.2419973     Document Type: Article

References (58)

1. F. Perronnin and C. Dance, "Fisher kernels on visual vocabularies for image categorization," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2007, pp. 1-8.
2. H. Jégou, M. Douze, C. Schmid, and P. Pérez, "Aggregating local descriptors into a compact image representation," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2010, pp. 3304-3311.
3. D. G. Lowe, "Distinctive image features from scale invariant keypoints," Int. J. Comput. Vis., vol. 60, no. 2, pp. 91-110, 2004.
4. H. Bay et al., "SURF: Speeded up robust features," in Proc. ECCV, 2006, pp. 404-417.
5. F. Perronnin, Y. Liu, J. Sánchez, and H. Poirier, "Large-scale image retrieval with compressed Fisher vectors," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2010, pp. 3384-3391.
6. D. Chen et al., "Residual enhanced visual vector as a compact signature for mobile visual search," Signal Process., vol. 93, no. 8, pp. 2316-2327, 2013.
7. J. Lin et al., "Rate-adaptive compact fisher codes for mobile visual search," IEEE Signal Process. Lett., vol. 21, no. 2, pp. 195-198, Feb. 2014.
8. Evaluation Framework for Compact Descriptors for Visual Search, ISO/IEC JTC1/SC29/WG11/N12202, 2011.
9. Text of ISO/IEC DIS 15938-13 Compact Descriptors for Visual Search, ISO/IEC JTC1/SC29/WG11/W14392, 2014.
10. B. Girod et al., "Mobile visual search," IEEE Signal Process. Mag., vol. 28, no. 4, pp. 61-76, Jul. 2011.
11. W. Zhou et al., "Towards codebook-free: Scalable cascaded hashing for mobile image search," IEEE Trans. Multimedia, vol. 28, no. 4, pp. 61-76, Jul. 2014.
12. A. A. and P. I., "Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions," in Proc. FOCS, 2006, pp. 459-468.
13. M. Charikar, "Similarity estimation techniques from rounding algorithms," in Proc. STOC, 2002, pp. 380-388.
14. C. Silpa-Anan and R. Hartley, "Optimised KD-trees for fast image descriptor matching," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2008, 2008, pp. 1-8.
15. L.-Y. Duan, J. Lin, J. Chen, T. Huang, and W. Gao, "Compact descriptors for visual search," IEEE Multimedia Mag., vol. 21, no. 3, pp. 30-40, Jul.-Sep. 2014.
16. T. Trzcinski et al., "Thick boundaries in binary space and their influence on nearest-neighbor search," Pattern Recog. Lett., vol. 33, no. 16, pp. 2173-2180, Dec. 2012.
17. E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, "ORB: An efficient alternative to SIFT or SURF," in Proc. IEEE Int. Conf. Comput. Vis., Nov. 2011, pp. 2564-2571.
18. S. Zhang, Q. Tian, Q. Huang, W. Gao, and Y. Rui, "USB: Ultrashort binary descriptor for fast visual matching and retrieval," IEEE Trans. Image Process., vol. 23, no. 8, pp. 3671-3683, Aug. 2014.
19. M. Calonder et al., "Brief: Binary robust independent elementary features," in Proc. Eur. Conf. Comput. Vis., 2010, pp. 778-792.
20. M. M. Esmaeili, R. K. Ward, and M. Fatourechi, "A fast approximate nearest neighbor search algorithm in the hamming space," IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 2, pp. 2481-2488, Dec. 2012.
21. J. Sivic and A. Zisserman, "Video Google: A text retrieval approach to object matching in videos," in Proc. IEEE Int. Conf. Comput. Vis., Oct. 2003, vol. 2, pp. 1470-1477.
22. D. Nister and H. Stewenius, "Scalable recognition with a vocabulary tree," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2006, vol. 2, pp. 2161-2168.
23. J. Philbin, O. Chum, and M. Isard et al., "Object retrieval with large vocabularies and fast spatial matching," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2007, pp. 1-8.
24. 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 Proc. IEEE Int. Conf. Comput. Vis., Oct. 2007, pp. 1-8.
25. J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman, "Lost in quantization: Improving particular object retrieval in large scale image databases," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2008, pp. 1-8.
26. H. Jégou, M. Douze, and C. Schmid, "Hamming embedding and weak geometric consistency for large scale image search," in Proc. ECCV, 2008, pp. 304-317.
27. G. Tolias, Y. Avrithis, and H. Jégou, "To aggregate or not to aggregate: Selective match kernels for image search," in Proc. IEEE Int. Conf. Comput. Vis., Dec. 2013, pp. 1401-1408.
28. H. Jégou, M. Douze, and C. Schmid, "Improving bag-of-features for large scale image search," Int. J. Comput. Vis., vol. 87, no. 3, pp. 316-336, May 2010.
29. 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.
30. H. Jégou et al., "Aggregating local images descriptors into compact codes," IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 9, pp. 1704-1716, Sep. 2012.
31. F. Perronnin and H. Jégou, "CVPR tutorial: Large-scale visual recognition," presented at the IEEE Conf. Comput. Vis. Pattern Recog. Tut., Jun. 2012.
32. Y. Weiss, A. Torralba, and R. Fergus, "Spectral hashing," in Proc. NIPS, 2008, pp. 1753-1760.
33. A. Torralba, R. Fergus, and Y. Weiss, "Small codes and large databases for recognition," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2008, pp. 1-8.
34. Y. Gong and S. Lazebnik, "Iterative quantization: A procrustean approach to learning binary codes," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2011, pp. 817-824.
35. M. Norouzi and D. Fleet, "Minimal loss hashing for compact binary codes," in Proc. ICML, 2011, pp. 353-360.
36. R. Ji et al., "Location discriminative vocabulary coding for mobile landmark search," Int. J. Comput. Vis., vol. 96, no. 3, pp. 290-314, Feb. 2012.
37. Y.-G. Jiang, J. Wang, X. Xue, and S.-F. Chang, "Query-adaptive image search with hash codes," IEEE Trans. Multimedia, vol. 15, no. 2, pp. 442-453, Feb. 2013.
38. T. Jaakkola and D. Haussler, "Exploiting generative models in discriminative classifier," in Proc. NIPS, 1999, pp. 487-493.
39. Y. Gong, S. Kumar, H. Rowley, and S. Lazebnik, "Learning binary codes for high-dimensional data using bilinear projections," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2013, pp. 484-491.
40. H. Jégou, M. Douze, and C. Schmid, "Product quantization for nearest neighbor search," IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 1, pp. 117-128, Jan. 2011.
41. M. Norouzi, A. Punjani, and D. Fleet, "Fast search in hamming space with multi-index hashing," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2012, pp. 3108-3115.
42. R. Arandjelovic and A. Zisserman, "Three things everyone should know to improve object retrieval," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2012, pp. 2911-2918.
43. R. Arandjelovic and A. Zisserman, "All About VLAD," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2013, pp. 1578-1585.
44. M. Douze, A. Ramisa, and C. Schmid, "Combining attributes and Fisher vectors for efficient image retrieval," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2011, pp. 745-752.
45. J. Delhumeau et al., "Revisiting the VLAD image representation," in Proc. ACM Multimedia, 2013, pp. 653-656.
46. H. Jégou and A. Zisserman, "Triangulation embedding and democratic aggregation for image search," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2014, pp. 3310-3317.
47. T. Cover and J. Thomas, Elements of Information Theory. New York, NY, USA: Wiley, 1991.
48. F. Fleuret, "Fast binary feature selection with conditional mutual information," J. Mach. Learn. Res., vol. 5, pp. 1531-1555, Dec. 2004.
49. G. Tolias, T. Furon, and H. Jégou, "Orientation covariant aggregation of local descriptors with embeddings," in Proc. ECCV, 2014, pp. 382-397.
50. V. Chandrasekhar et al., "The Stanford mobile visual search dataset," in Proc. ACM Multimedia Syst. Conf., 2011, pp. 117-122.
51. J. Friedman, J. Bentley, and R. Finkel, "An algorithm for finding best matches in logarithmic expected time," ACM Trans. Math. Softw., vol. 3, no. 3, pp. 1977209-226.
52. C. Silpa-Anan and R. Hartley, "Optimised KD-trees for fast image descriptor matching," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2008, pp. 1-8.
53. T. Ge, K. He, Q. Ke, and J. Sun, "Optimized product quantization for approximate nearest neighbor search," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2013, pp. 2946-2953.
54. Y. Kalantidis and Y. Avrithis, "Locally optimized product quantization for approximate nearest neighbor search," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2014, pp. 2329-2336.
55. 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 Recog., Jun. 2013, pp. 2938-2945.
56. J.-P. Heo, Z. Lin, and S.-E. Yoon, "Distance encoded product quantization," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2014, pp. 2139-2146.
57. M. Jain et al., "Asymmetric hamming embedding: Taking the best of our bits for large scale image search," in Proc. ACM Multimedia, 2011, pp. 1441-1444.
58. A. Mikulik et al., "Learning a fine vocabulary," in Proc. ECCV, 2010, pp. 1-14.