Ying Wei
Qiang Yang
ABSTRACT
Hashing has enjoyed a great success in large-scale similarity search. Recently, researchers have studied the multi-modal hashing to meet the need of similarity search across different types of media. However, most of the existing methods are applied to search across multi-views among which explicit bridge information is provided. Given a heterogeneous media search task, we observe that abundant multi-view data can be found on the Web which can serve as an auxiliary bridge. In this paper, we propose a Heterogeneous Translated Hashing (HTH) method with such auxiliary bridge incorporated not only to improve current multi-view search but also to enable similarity search across heterogeneous media which have no direct correspondence. HTH simultaneously learns hash functions embedding heterogeneous media into different Hamming spaces, and translators aligning these spaces. Unlike almost all existing methods that map heterogeneous data in a common Hamming space, mapping to different spaces provides more flexible and discriminative ability. We empirically verify the effectiveness and efficiency of our algorithm on two real world large datasets, one publicly available dataset of Flickr and the other MIRFLICKR-Yahoo Answers dataset.
Supplemental Material
- A. Andoni and P. Indyk. Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. In FOCS, pages 459--468, 2006. Google Scholar
Digital Library
- J. L. Bentley. Multidimensional binary search trees used for associative searching. Communications of the ACM, 18(9):509--517, 1975. Google ScholarDigital Library
- D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. the Journal of machine Learning research, 3:993--1022, 2003. Google ScholarDigital Library
- M. Bronstein, A. Bronstein, F. Michel, and N. Paragios. Data fusion through cross-modality metric learning using similarity-sensitive hashing. In CVPR, pages 3594--3601, 2010.Google ScholarCross Ref
- T.-S. Chua, J. Tang, R. Hong, H. Li, Z. Luo, and Y.-T. Zheng. Nus-wide: A real-world web image database from national university of singapore. In VLDB, pages 48:1--48:9, 2009.Google Scholar
- M. Datar, N. Immorlica, P. Indyk, and V. S. Mirrokni. Locality-sensitive hashing scheme based on p-stable distributions. In SCG, pages 253--262, 2004. Google ScholarDigital Library
- A. Gionis, P. Indyk, and R. Motwani. Similarity search in high dimensions via hashing. In VLDB, pages 518--529, 1999. Google ScholarDigital Library
- Y. Gong and S. Lazebnik. Iterative quantization: A procrustean approach to learning binary codes. In CVPR, pages 817--824, 2011. Google ScholarDigital Library
- M. J. Huiskes and M. S. Lew. The mir flickr retrieval evaluation. In MIR, 2008. Google ScholarDigital Library
- B. Kulis and K. Grauman. Kernelized locality-sensitive hashing for scalable image search. In CVPR, pages 2130--2137, 2009.Google ScholarCross Ref
- B. Kulis, P. Jain, and K. Grauman. Fast similarity search for learned metrics. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 31(12):2143--2157, 2009. Google ScholarDigital Library
- S. Kumar and R. Udupa. Learning hash functions for cross-view similarity search. In IJCAI, pages 1360--1365, 2011. Google ScholarDigital Library
- W. Liu, J. Wang, S. Kumar, and S.-F. Chang. Hashing with graphs. In ICML, pages 1--8, June 2011.Google ScholarDigital Library
- Y. Mu, J. Shen, and S. Yan. Weakly-supervised hashing in kernel space. In CVPR, pages 3344--3351, 2010.Google ScholarCross Ref
- M. Ou, P. Cui, F. Wang, J. Wang, W. Zhu, and S. Yang. Comparing apples to oranges: A scalable solution with heterogeneous hashing. In KDD, pages 230--238, 2013. Google ScholarDigital Library
- N. Quadrianto and C. Lampert. Learning multi-view neighborhood preserving projections. In ICML, pages 425--432, 2011.Google Scholar
- M. Raginsky and S. Lazebnik. Locality-sensitive binary codes from shift-invariant kernels. In NIPS, pages 1509--1517. 2009.Google ScholarDigital Library
- R. Salakhutdinov and G. Hinton. Semantic hashing. International Journal of Approximate Reasoning, 50(7):969--978, 2009. Google ScholarDigital Library
- G. Shakhnarovich, P. Viola, and T. Darrell. Fast pose estimation with parameter-sensitive hashing. In ICCV, pages 750--757, 2003. Google ScholarDigital Library
- S. Shalev-Shwartz, Y. Singer, and N. Srebro. Pegasos: Primal estimated sub-gradient solver for svm. In ICML, pages 807--814, 2007. Google ScholarDigital Library
- S. Shalev-Shwartz, Y. Singer, N. Srebro, and A. Cotter. Pegasos: primal estimated sub-gradient solver for svm. Mathematical Programming, 127(1):3--30, 2011. Google ScholarDigital Library
- J. Song, Y. Yang, Y. Yang, Z. Huang, and H. T. Shen. Inter-media hashing for large-scale retrieval from heterogeneous data sources. In SIGMOD, pages 785--796, 2013. Google ScholarDigital Library
- C. Strecha, A. Bronstein, M. Bronstein, and P. Fua. Ldahash: Improved matching with smaller descriptors. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 34(1):66--78, 2012. Google ScholarDigital Library
- J. K. Uhlmann. Satisfying general proximity/similarity queries with metric trees. Information processing letters, 40(4):175--179, 1991.Google Scholar
- J. Wang, S. Kumar, and S.-F. Chang. Semi-supervised hashing for scalable image retrieval. In CVPR, pages 3424--3431, 2010.Google ScholarCross Ref
- R. Weber, H.-J. Schek, and S. Blott. A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces. In VLDB, pages 194--205, 1998. Google ScholarDigital Library
- Y. Weiss, A. Torralba, and R. Fergus. Spectral hashing. In NIPS, pages 1753--1760, 2008.Google ScholarDigital Library
- F. Wu, Z. Yu, Y. Yang, S. Tang, Y. Zhang, and Y. Zhuang. Sparse multi-modal hashing. Multimedia, IEEE Transactions on, 16(2):427--439, 2014.Google ScholarDigital Library
- A. L. Yuille, A. Rangarajan, and A. Yuille. The concave-convex procedure (cccp). Advances in neural information processing systems, 2:1033--1040, 2002.Google Scholar
- D. Zhai, H. Chang, Y. Zhen, X. Liu, X. Chen, and W. Gao. Parametric local multimodal hashing for cross-view similarity search. In IJCAI, pages 2754--2760, 2013. Google ScholarDigital Library
- D. Zhang, J. Wang, D. Cai, and J. Lu. Self-taught hashing for fast similarity search. In SIGIR, pages 18--25, 2010. Google ScholarDigital Library
- Y. Zhen and D. Y. Yeung. Co-regularized hashing for multimodal data. In NIPS, pages 1385--1393. 2012.Google ScholarDigital Library
- Y. Zhen and D. Y. Yeung. A probabilistic model for multimodal hash function learning. In KDD, pages 940--948, 2012. Google ScholarDigital Library
- X. Zhu, Z. Huang, H. T. Shen, and X. Zhao. Linear cross-modal hashing for efficient multimedia search. In MM, pages 143--152, 2013. Google ScholarDigital Library
Index Terms
- Scalable heterogeneous translated hashing
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