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IEEE TRANSACTIONS ON IMAGE PROCESSING.VOL.XX,NO.X.XXX 2019 12 [22]J.Wang,T.Zhang,J.Song,N.Sebe,and H.T.Shen,"A survey on learning to hash,"TPAMI,vol.40.no.4,pp.769-790,2018. [23]X.Lu,X.Zheng,and X.Li."Latent semantic minimal hashing for image retrieval,"TIP,vol.26,no.1,pp.355-368.2017 [24]F.Shen,C.Shen,W.Liu,and H.T.Shen,"Supervised discrete hashing," 4 48132 84 48182 in CVPR,2015,Pp.37-45. (25]F.Shen.X.Zhou,Y.Yang,J.Song,H.T.Shen.and D.Tao,"A fast (a)IAPR-TC12. (b)MIRFLICKR-25K. (c)NUS-WIDE optimization method for general binary code learning."TIP,vol.25. Fig.10.MAP values with different number of sampled points m on three no.12,Pp.5610-5621,2016. datasets. [26]D.Zhang and W.-J.Li,"Large-scale supervised multimodal hashing with semantic correlation maximization,"in AAA/,2014,pp.2177-2183. [27]G.Ding,Y.Guo,and J.Zhou,"Collective matrix factorization hashing VI.CONCLUSION for multimodal data,"in CVPR.2014,pp.2083-2090. In this paper,we propose a novel cross-modal hashing (28]X.Liu,L.Huang.C.Deng.B.Lang,and D.Tao,"Query-adaptive hash code ranking for large-scale multi-view visual search."TIP.vol.25 method,called discrete latent factor model based cross- n0.10,Pp.4514-4524,2016. modal hashing (DLFH),for cross-modal similarity search in [29]J.Song.Y.Yang.Z.Huang.H.T.Shen,and R.Hong,"Multiple feature large-scale datasets.DLFH is a discrete method which can hashing for real-time large scale near-duplicate video retrieval,"in MM. 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(53]T.Chua,J.Tang.R.Hong,H.Li,Z.Luo,and Y.Zheng."NUS-WIDE: [20]J.Wang.W.Liu,S.Kumar,and S.Chang,"Learning to hash for indexing a real-world web image database from national university of singapore," big data-A survey,"Proceedings of the IEEE.vol.104.no.1.pp.34-57 in C/VR,2009. 2016. [21]Y.Guo,G.Ding.L.Liu,J.Han,and L.Shao,"Learning to hash with optimized anchor embedding for scalable retrieval,"TIP,vol.26,no.3 Pp.1344-1354.2017.IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. XX, NO. X, XXX 2019 12 48 16 32 64 96 M A P 0.35 0.4 0.45 0.5 0.55 0.6 (a) IAPR-TC12. 48 16 32 64 96 M A P 0.65 0.7 0.75 0.8 0.85 (b) MIRFLICKR-25K. 48 16 32 64 96 M A P 0.6 0.65 0.7 0.75 0.8 (c) NUS-WIDE. Fig. 10. MAP values with different number of sampled points m on three datasets. VI. CONCLUSION In this paper, we propose a novel cross-modal hashing method, called discrete latent factor model based cross￾modal hashing (DLFH), for cross-modal similarity search in large-scale datasets. DLFH is a discrete method which can directly learn the binary hash codes, and at the same time it is efficient. Experiments show that DLFH can significantly outperform relaxation-based continuous methods in terms of accuracy, but with a comparable training speed. Furthermore, DLFH can significantly outperform existing discrete methods in terms of both accuracy and training speed. REFERENCES [1] M. Datar, N. Immorlica, P. Indyk, and V. S. Mirrokni, “Locality-sensitive hashing scheme based on p-stable distributions,” in SCG, 2004, pp. 253– 262. [2] A. Andoni and P. Indyk, “Near-optimal hashing algorithms for approxi￾mate nearest neighbor in high dimensions,” in FOCS, 2006, pp. 459–468. [3] A. Andoni and I. P. Razenshteyn, “Optimal data-dependent hashing for approximate near neighbors,” in STOC, 2015, pp. 793–801. [4] B. Kulis and K. Grauman, “Kernelized locality-sensitive hashing for scalable image search,” in ICCV, 2009, pp. 2130–2137. [5] J. Wang, O. Kumar, and S. 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