Democratic diffusion aggregation for image retrieval

IEEE Transactions on Multimedia

Volumn 18, Issue 8, 2016, Pages 1661-1674

  Gao, Zhanning ^a   Xue, Jianru ^a   Zhou, Wengang ^b   Pang, Shanmin ^a   Tian, Qi ^c  

^a XI'AN JIAOTONG UNIVERSITY   (China)

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

^c One UTSA Circle   (United States)

Author keywords

Democratic diffusion aggregation (DDA); image retrieval; query fusion

Indexed keywords

AGGLOMERATION; CONTENT BASED RETRIEVAL; DIFFUSION; IMAGE FUSION; ITERATIVE METHODS;

AGGREGATION METHODS; CONTENT BASED IMAGE RETRIEVAL; DEMOCRATIC DIFFUSION AGGREGATION (DDA); HIGHER EFFICIENCY; ITERATIVE OPTIMIZATION; RETRIEVAL ACCURACY; RETRIEVAL PERFORMANCE; WEIGHTING COEFFICIENT;

IMAGE RETRIEVAL;

EID: 84979528664     PISSN: 15209210     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMM.2016.2568748     Document Type: Article

References (53)

1. 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.
2. R. Arandjelovic and A. Zisserman, "All about VLAD," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2013, pp. 1578-1585.
3. A. Babenko and V. Lempitsky, "The inverted multi-index," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2012, pp. 3069-3076.
4. L. Bo and C. Sminchisescu, "Efficient match kernel between sets of features for visual recognition," in Proc. Adv. Neural Inf. Process. Syst., 2009, pp. 135-143.
5. Y.-L. Boureau, J. Ponce, and Y. LeCun, "A theoretical analysis of feature pooling in visual recognition," in Proc. Int. Conf. Mach. Learn., 2010, pp. 111-118.
6. L. Chu, S. Jiang, S. Wang, Y. Zhang, and Q. Huang, "Robust spatial consistency graphmodel for partial duplicate image retrieval," IEEE Trans. Multimedia, vol. 15, no. 8, pp. 1982-1996, Jun. 2013.
7. O. Chum and J. Matas, "Unsupervised discovery of co-occurrence in sparse high dimensional data," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2010, pp. 3416-3423.
8. 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.
9. R. G. Cinbis, J. Verbeek, and C. Schmid, "Image categorization using fisher kernels of non-IID image models," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2012, pp. 2184-2191.
10. R. R. Coifman and S. Lafon, "Diffusion maps," Appl. Comput. Harmonic Anal., vol. 21, no. 1, pp. 5-30, 2006.
11. J. Delhumeau, P.-H. Gosselin, H. Jegou, and P. Perez, "Revisiting the VLAD image representation," in Proc. ACM Int. Conf. Multimedia, 2013, pp. 653-656.
12. 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.
13. L.-Y. Duan, et al., "Overview of theMPEG-CDVS standard," IEEE Trans. Image Process., vol. 25, no. 1, pp. 179-194, Jan. 2016.
14. L.-Y. Duan, J. Lin, Z. Wang, T. Huang, and W. Gao, "Weighted component hashing of binary aggregated descriptors for fast visual search," IEEE Trans. Multimedia, vol. 17, no. 6, pp. 828-842, Jun. 2015.
15. Z. Gao, J. Xue, W. Zhou, S. Pang, and Q. Tian, "Fast democratic aggregation and query fusion for image search," in Proc. ACM Int. Conf. Multimedia Retrieval, 2015, pp. 35-42.
16. T. Ge, K. He, and J. Sun, "Product sparse coding," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2014, pp. 939-946.
17. T. Ge, Q. Ke, and J. Sun, "Sparse-coded features for image retrieval," in Proc. Brit. Mach. Vis. Conf., Sep. 2013, pp. 132.1-132.11.
18. Y. Gong, L. Wang, R. Guo, and S. Lazebnik, "Multi-scale orderless pooling of deep convolutional activation features," in Proc. Eur. Conf. Comput. Vis., 2014, pp. 392-407.
19. H. Jegou and O. Chum, "Negative evidences and co-occurences in image retrieval: The benefit of PCA and whitening," in Proc. Eur. Conf. Comput. Vis., 2012, pp. 774-787.
20. H. Jegou, M. Douze, and C. Schmid, "Hamming embedding and weak geometric consistency for large scale image search," in Proc. Eur. Conf. Comput. Vis., 2008, pp. 304-317.
21. H. Jegou, M. Douze, and C. Schmid, "On the burstiness of visual elements," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2009, pp. 1169-1176.
22. H. Jegou, 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, 2010.
23. H. Jegou, 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.
24. H. Jegou, et al., "Aggregating local image descriptors into compact codes," IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 9, pp. 1704-1716, Sep. 2012.
25. H. Jegou and A. Zisserman, "Triangulation embedding and democratic aggregation for image search," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2014, pp. 3310-3317.
26. P. A. Knight, "The sinkhorn-knopp algorithm: Convergence and applications," J. Matrix Anal. Appl., vol. 30, no. 1, pp. 261-275, 2008.
27. S. Lafon and A. B. Lee, "Diffusion maps and coarse-graining: A unified framework for dimensionality reduction, graph partitioning, and data set parameterization," IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 9, pp. 1393-1403, Sep. 2006.
28. P. Li, M. Wang, J. Cheng, C. Xu, and H. Lu, "Spectral hashing with semantically consistent graph for image indexing," IEEE Trans.Multimedia, vol. 15, no. 1, pp. 141-152, Jan. 2013.
29. 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.
30. W. Liu, J. Wang, S. Kumar, and S.-F. Chang, "Hashing with graphs," in Proc. Int. Conf. Mach. Learn., Jun. 2011, pp. 1-8.
31. Z. Liu, H. Li, W. Zhou, R. Hong, and Q. Tian, "Uniting keypoints: Local visual information fusion for large-scale image search," IEEE Trans. Multimedia, vol. 17, no. 4, pp. 538-548, Apr. 2015.
32. D. G. Lowe, "Distinctive image features from scale-invariant keypoints," Int. J. Comput. Vis., vol. 60, no. 2, pp. 91-110, 2004.
33. K. Mikolajczyk and C. Schmid, "Scale and affine invariant interest point detectors," Int. J. Comput. Vis., vol. 60, no. 1, pp. 63-86, 2004.
34. N. Murray and F. Perronnin, "Generalized max pooling," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2014, pp. 2473-2480.
35. A. Y. Ng, M. I. Jordan, and Y. Weiss, "On spectral clustering: Analysis and an algorithm," Advances Neural Inf., vol. 2, pp. 849-856, 2002.
36. M. Perdoch, O. Chum, and J. Matas, "Efficient representation of local geometry for large scale object retrieval," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2009, pp. 9-16.
37. F. Perronnin, Y. Liu, J. Sanchez, and H. Poirier, "Large-scale image retrieval with compressed fisher vectors," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2010, pp. 3384-3391.
38. J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman, "Object retrieval with large vocabularies and fast spatial matching," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2007, pp. 1-8.
39. 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.
40. B. Safadi and G. Quenot, "Descriptor optimization for multimedia indexing and retrieval," in Proc.Workshop Content Based Multimedia Indexing, 2013, pp. 65-71.
41. 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, pp. 1470-1477.
42. X. Tian, Q. Jia, and T. Mei, "Query difficulty estimation for image search with query reconstruction error," IEEE Trans. Multimedia, vol. 17, no. 1, pp. 79-91, Jan. 2015.
43. G. Tolias, T. Furon, and H. Jegou, "Orientation covariant aggregation of local descriptors with embeddings," in Proc. Eur. Conf. Comput. Vis., 2014, pp. 382-397.
44. G. Tolias and H. Jegou, "Visual query expansionwith or without geometry: Refining local descriptors by feature aggregation," Pattern Recog., vol. 47, no. 10, pp. 3466-3476, 2014.
45. S. Wang and S. Jiang, "INSTRE: A new benchmark for instance-level object retrieval and recognition," ACM Trans. Multimedia Comput., Commun. Appl., vol. 11, no. 3, p. 37, 2015.
46. S. A. Winder and M. Brown, "Learning local image descriptors," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2007, pp. 1-8.
47. X. Yang, L. Prasad, and L. J. Latecki, "Affinity learning with diffusion on tensor product graph," IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 1, pp. 28-38, Jan. 2013.
48. W. L. Zhao, H. Jegou, and G. Gravier, "Oriented pooling for dense and non-dense rotation-invariant features," in Proc. Brit. Mach. Vis. Conf., 2013, pp. 99.1-99.11.
49. L. Zheng, S. Wang, Z. Liu, and Q. Tian, "LP-norm IDF for large scale image search," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2013, pp. 1626-1633.
50. W. Zhou, H. Li, R. Hong, Y. Lu, and Q. Tian, "BSIFT: Towards dataindependent codebook for large scale image search," IEEE Trans. Image Process., vol. 24, no. 3, pp. 967-979, Mar. 2015.
51. W. Zhou, Y. Lu, H. Li, Y. Song, and Q. Tian, "Spatial coding for large scale partial-duplicate web image search," in Proc. ACM Int. Conf. Multimedia, 2010, pp. 511-520.
52. W. Zhou, et al., "Towards codebook-free: Scalable cascaded hashing for mobile image search," IEEE Trans. Multimedia, vol. 16, no. 3, pp. 601-611, Apr. 2014.
53. W. Zhou, et al., "Scalable feature matching by dual cascaded scalar quantization for image retrieval," IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 1, pp. 159-171, Jan. 2016.