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References [Liu et al.,2014]Wei Liu,Cun Mu,Sanjiv Kumar,and Shih- [Andoni and Indyk.2008]Alexandr Andoni and Piotr Indyk. Fu Chang.Discrete graph hashing.In Proceedings of Near-optimal hashing algorithms for approximate nearest the Advances in Neural Information Processing Systems, neighbor in high dimensions.Communications on ACM. pages3419-3427,2014. 51(1):117-122.2008. [Norouzi and Fleet,2011]Mohammad Norouzi and David J. [Andoni,2009 Alexandr Andoni.Nearest Neighbor Search: Fleet.Minimal loss hashing for compact binary codes.In The Old,The New,and The Impossible.PhD thesis,Mas- Proceedings of the International Conference on Machine sachusetts Institute of Technology,2009. Learning,pages 353-360,2011. [Datar et al.,2004]Mayur Datar,Nicole Immorlica,Piotr [Shrivastava and Li,2014]Anshumali Shrivastava and Ping Indyk,and Vahab S.Mirrokni.Locality-sensitive hash- Li.Asymmetric Ish (alsh)for sublinear time maximum ing scheme based on p-stable distributions.In Proceed- inner product search (mips).In Proceedings of the Ad- ings of the Annual Symposium on Computational Geome- vances in Neural Information Processing Systems,pages ty,pages253-262,2004. 2321-2329.2014 [Gionis et al.,1999]Aristides Gionis,Piotr Indyk,and Ra- [Song et al,2013]Jingkuan Song,Yang Yang,Yi Yang,Z- jeev Motwani.Similarity search in high dimensions via i Huang,and Heng Tao Shen.Inter-media hashing for hashing.In Proceedings of the International Conference large-scale retrieval from heterogeneous data sources.In on Very Large Data Bases,pages 518-529,1999. Proceedings of the ACM SIGMOD International Confer- [Gong and Lazebnik,2011]Yunchao Gong and Svetlana ence on Management of Data,pages 785-796,2013. Lazebnik.Iterative quantization:A procrustean approach [Wang et al.,2010a]Jun Wang,Ondrej Kumar,and Shih-Fu to learning binary codes.In Proceedings of the IEEE Chang.Semi-supervised hashing for scalable image re- Conference on Computer Vision and Pattern Recognition, trieval.In Proceedings of the IEEE Conference on Com- pages817-824,2011 puter Vision and Pattern Recognition,pages 3424-3431, Huiskes et al..2010]Mark J.Huiskes.B.Thomee.and 2010 Michael S.Lew.New trends and ideas in visual concep- [Wang et al.,2010b]Jun Wang,Sanjiv Kumar,and Shih-Fu t detection:The mir flickr retrieval evaluation initiative. Chang.Sequential projection learning for hashing with In Proceedings of the ACM International Conference on compact codes.In Proceedings of the International Con- Multimedia Information Retrieval,pages 527-536,2010. ference on Machine Learning,pages 1127-1134,2010. [Indyk and Motwani,1998]Piotr Indyk and Rajeev Mot- [Weiss et al.,2008]Yair Weiss,Antonio Torralba,and wani.Approximate nearest neighbors:Towards removing Robert Fergus.Spectral hashing.In Proceedings ofthe Ad- the curse of dimensionality.In Proceedings of the Annu- vances in Neural Information Processing Systems,pages al ACM Symposium on Theory of Computing,pages 604 1753-1760,2008 613.1998. [Xu et al.,2013]Bin Xu,Jiajun Bu,Yue Lin,Chun Chen, [Kong and Li,2012]Weihao Kong and Wu-Jun Li.Isotropic Xiaofei He,and Deng Cai.Harmonious hashing.In Pro- hashing.In Proceedings of the Advances in Neural Infor- ceedings of the International Joint Conference on Artificial mation Processing Systems,pages 1655-1663,2012. Intelligence,2013. Kulis and Darrell,2009]Brian Kulis and Trevor Darrell. [Zhang and Li,2014]Dongqing Zhang and Wu-Jun Li. Learning to hash with binary reconstructive embeddings. Large-scale supervised multimodal hashing with seman- In Proceedings of the Advances in Neural Information tic correlation maximization.In Proceedings of the AAAl Processing Systems,pages 1042-1050,2009 Conference on Artificial Intelligence,pages 2177-2183, [Kulis and Grauman,2009]Brian Kulis and Kristen Grau- 2014. man.Kernelized locality-sensitive hashing for scalable [Zhang et al.,2014]Peichao Zhang,Wei Zhang,Wu-Jun Li, image search.In Proceedings of the IEEE International and Minyi Guo.Supervised hashing with latent factor Conference on Computer Vision,pages 2130-2137,2009. models.In Proceedings of the International ACM SIGIR [Lin et al.,2014]Guosheng Lin,Chunhua Shen,Qinfeng Conference on Research and Development in Information Shi,Anton van den Hengel,and David Suter.Fast su- Retrieval,pages 173-182,2014. pervised hashing with decision trees for high-dimensional [Zhen and Yeung,2012]Yi Zhen and Dit-Yan Yeung.A data.In Proceedings of the IEEE Conference on Computer probabilistic model for multimodal hash function learning. Vision and Pattern Recognition,pages 1971-1978,2014. In Proceedings of the ACM SIGKDD International Con- Liu et al.,2011]Wei Liu,Jun Wang,Sanjiv Kumar,and ference on Knowledge Discovery and Data Mining,pages Shih-Fu Chang.Hashing with graphs.In Proceedings of 940-948.2012 the International Conference on Machine Learning,2011. Zhu et al.,2013]Xiaofeng Zhu,Zi Huang,Heng Tao Shen, [Liu et al.,2012]Wei Liu,Jun Wang,Rongrong Ji,Yu-Gang and Xin Zhao.Linear cross-modal hashing for efficient Jiang,and Shih-Fu Chang.Supervised hashing with ker- multimedia search.In Proceedings of the ACM Interna- nels.In Proceedings of the IEEE Conference on Computer tional Conference on Multimedia,pages 143-152,2013. Vision and Pattern Recognition,pages 2074-2081,2012.References [Andoni and Indyk, 2008] Alexandr Andoni and Piotr Indyk. Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. Communications on ACM, 51(1):117–122, 2008. [Andoni, 2009] Alexandr Andoni. Nearest Neighbor Search: The Old, The New, and The Impossible. PhD thesis, Mas￾sachusetts Institute of Technology, 2009. [Datar et al., 2004] Mayur Datar, Nicole Immorlica, Piotr Indyk, and Vahab S. Mirrokni. Locality-sensitive hash￾ing scheme based on p-stable distributions. In Proceed￾ings of the Annual Symposium on Computational Geome￾try, pages 253–262, 2004. [Gionis et al., 1999] Aristides Gionis, Piotr Indyk, and Ra￾jeev Motwani. Similarity search in high dimensions via hashing. In Proceedings of the International Conference on Very Large Data Bases, pages 518–529, 1999. [Gong and Lazebnik, 2011] Yunchao Gong and Svetlana Lazebnik. Iterative quantization: A procrustean approach to learning binary codes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 817–824, 2011. [Huiskes et al., 2010] Mark J. Huiskes, B. Thomee, and Michael S. Lew. New trends and ideas in visual concep￾t detection: The mir flickr retrieval evaluation initiative. In Proceedings of the ACM International Conference on Multimedia Information Retrieval, pages 527–536, 2010. [Indyk and Motwani, 1998] Piotr Indyk and Rajeev Mot￾wani. Approximate nearest neighbors: Towards removing the curse of dimensionality. In Proceedings of the Annu￾al ACM Symposium on Theory of Computing, pages 604– 613, 1998. [Kong and Li, 2012] Weihao Kong and Wu-Jun Li. Isotropic hashing. In Proceedings of the Advances in Neural Infor￾mation Processing Systems, pages 1655–1663, 2012. [Kulis and Darrell, 2009] Brian Kulis and Trevor Darrell. Learning to hash with binary reconstructive embeddings. In Proceedings of the Advances in Neural Information Processing Systems, pages 1042–1050, 2009. [Kulis and Grauman, 2009] Brian Kulis and Kristen Grau￾man. Kernelized locality-sensitive hashing for scalable image search. In Proceedings of the IEEE International Conference on Computer Vision, pages 2130–2137, 2009. [Lin et al., 2014] Guosheng Lin, Chunhua Shen, Qinfeng Shi, Anton van den Hengel, and David Suter. Fast su￾pervised hashing with decision trees for high-dimensional data. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1971–1978, 2014. [Liu et al., 2011] Wei Liu, Jun Wang, Sanjiv Kumar, and Shih-Fu Chang. Hashing with graphs. In Proceedings of the International Conference on Machine Learning, 2011. [Liu et al., 2012] Wei Liu, Jun Wang, Rongrong Ji, Yu-Gang Jiang, and Shih-Fu Chang. Supervised hashing with ker￾nels. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2074–2081, 2012. [Liu et al., 2014] Wei Liu, Cun Mu, Sanjiv Kumar, and Shih￾Fu Chang. Discrete graph hashing. In Proceedings of the Advances in Neural Information Processing Systems, pages 3419–3427, 2014. [Norouzi and Fleet, 2011] Mohammad Norouzi and David J. Fleet. Minimal loss hashing for compact binary codes. In Proceedings of the International Conference on Machine Learning, pages 353–360, 2011. [Shrivastava and Li, 2014] Anshumali Shrivastava and Ping Li. Asymmetric lsh (alsh) for sublinear time maximum inner product search (mips). In Proceedings of the Ad￾vances in Neural Information Processing Systems, pages 2321–2329, 2014. [Song et al., 2013] Jingkuan Song, Yang Yang, Yi Yang, Z￾i Huang, and Heng Tao Shen. Inter-media hashing for large-scale retrieval from heterogeneous data sources. In Proceedings of the ACM SIGMOD International Confer￾ence on Management of Data, pages 785–796, 2013. [Wang et al., 2010a] Jun Wang, Ondrej Kumar, and Shih-Fu Chang. Semi-supervised hashing for scalable image re￾trieval. In Proceedings of the IEEE Conference on Com￾puter Vision and Pattern Recognition, pages 3424–3431, 2010. [Wang et al., 2010b] Jun Wang, Sanjiv Kumar, and Shih-Fu Chang. Sequential projection learning for hashing with compact codes. In Proceedings of the International Con￾ference on Machine Learning, pages 1127–1134, 2010. [Weiss et al., 2008] Yair Weiss, Antonio Torralba, and Robert Fergus. Spectral hashing. In Proceedings of the Ad￾vances in Neural Information Processing Systems, pages 1753–1760, 2008. [Xu et al., 2013] Bin Xu, Jiajun Bu, Yue Lin, Chun Chen, Xiaofei He, and Deng Cai. Harmonious hashing. In Pro￾ceedings of the International Joint Conference on Artificial Intelligence, 2013. [Zhang and Li, 2014] Dongqing Zhang and Wu-Jun Li. Large-scale supervised multimodal hashing with seman￾tic correlation maximization. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 2177–2183, 2014. [Zhang et al., 2014] Peichao Zhang, Wei Zhang, Wu-Jun Li, and Minyi Guo. Supervised hashing with latent factor models. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 173–182, 2014. [Zhen and Yeung, 2012] Yi Zhen and Dit-Yan Yeung. A probabilistic model for multimodal hash function learning. In Proceedings of the ACM SIGKDD International Con￾ference on Knowledge Discovery and Data Mining, pages 940–948, 2012. [Zhu et al., 2013] Xiaofeng Zhu, Zi Huang, Heng Tao Shen, and Xin Zhao. Linear cross-modal hashing for efficient multimedia search. In Proceedings of the ACM Interna￾tional Conference on Multimedia, pages 143–152, 2013
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