Abstract
Recent years have witnessed the promising capacity of hashing techniques in tackling nearest neighbor search because of the high efficiency in storage and retrieval. Data-independent approaches (e.g., Locality Sensitive Hashing) normally construct hash functions using random projections, which neglect intrinsic data properties. To compensate this drawback, learning-based approaches propose to explore local data structure and/or supervised information for boosting hashing performance. However, due to the construction of Laplacian matrix, existing methods usually suffer from the unaffordable training cost. In this paper, we propose a novel supervised hashing scheme, which has the merits of (1) exploring the inherent neighborhoods of samples; (2) significantly saving training cost confronted with massive training data by employing approximate anchor graph; as well as (3) preserving semantic similarity by leveraging pair-wise supervised knowledge. Besides, we integrate discrete constraint to significantly eliminate accumulated errors in learning reliable hash codes and hash functions. We devise an alternative algorithm to efficiently solve the optimization problem. Extensive experiments on two image datasets demonstrate that our proposed method is superior to the state-of-the-arts.
S. He and G. Ye contributed equally to this work.
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Bentley, J.L.: Multidimensional binary search trees used for associative searching. Commun. ACM 18(9), 509–517 (1975)
De Leeuw, J.: Applications of convex analysis to multidimensional scaling. Department of Statistics, UCLA (2005)
Gionis, A., Indyk, P., Motwani, R., et al.: Similarity search in high dimensions via hashing. In: VLDB, vol. 99, pp. 518–529 (1999)
Håstad, J.: Some optimal inapproximability results. J. ACM 48(4), 798–859 (2001)
Hu, M., Yang, Y., Shen, F., Zhang, L., Shen, H.T., Li, X.: Robust web image annotation via exploring multi-facet and structural knowledge. IEEE TIP 26(10), 4871–4884 (2017)
Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images (2009)
Kulis, B., Darrell, T.: Learning to hash with binary reconstructive embeddings. In: Proceedings of NIPS, pp. 1042–1050 (2009)
Kulis, B., Jain, P., Grauman, K.: Fast similarity search for learned metrics. IEEE TPAMI 31(12), 2143–2157 (2009)
Liu, W., Mu, C., Kumar, S., Chang, S-F.: Discrete graph hashing. In: Proceedings of NIPS, pp. 3419–3427 (2014)
Liu, W., Wang, J., Ji, R., Jiang, Y-G., Chang, S-F.: Supervised hashing with kernels. In: Proceedings of CVPR, pp. 2074–2081 (2012)
Liu, W., Wang, J., Kumar, S., Chang, S-F.: Hashing with graphs. In: Proceedings of ICML, pp. 1–8 (2011)
Luo, Y., Yang, Y., Shen, F., Huang, Z., Zhou, P., Shen, H.T.: Robust discrete code modeling for supervised hashing. Pattern Recognit. (2017). doi:10.1016/j.patcog.2017.02.034
Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. IJCV 42(3), 145–175 (2001)
Shen, F., Shen, C., Liu, W., Shen, H.T.: Supervised discrete hashing. In: Proceedings of CVPR, pp. 37–45 (2015)
Shen, F., Yang, Y., Liu, L., Liu, W., Tao, D., Shen, H.T.: Asymmetric binary coding for image search. IEEE Trans. Multimedia 19(9), 2022–2032 (2017). doi:10.1109/TMM.2017.2699863
Strecha, C., Bronstein, A.M., Bronstein, M.M., Fua, P.: LDAHash: improved matching with smaller descriptors. IEEE TPAMI 34(1), 66–78 (2012)
Torralba, A., Fergus, R., Freeman, W.T.: 80 million tiny images: a large data set for nonparametric object and scene recognition. IEEE TPAMI 30(11), 1958–1970 (2008)
Wang, J., Kumar, S., Chang, S.-F.: Semi-supervised hashing for large-scale search. IEEE TPAMI 34(12), 2393–2406 (2012)
Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. In: Proceedings of NIPS, pp. 1753–1760 (2008)
Wolf, L., Hassner, T., Maoz, I.: Face recognition in unconstrained videos with matched background similarity. In: Proceedings of CVPR, pp. 529–534. IEEE (2011)
Yang, Y., Luo, Y., Chen, W., Shen, F., Shao, J., Shen, H.T.: Zero-shot hashing via transferring supervised knowledge. In: Proceedings of ACM MM, pp. 1286–1295 (2016)
Yang, Y., Shen, F., Huang, Z., Shen, H.T., Li, X.: Discrete nonnegative spectral clustering. In: IEEE TKDE (2017)
Yang, Y., Shen, F., Shen, H.T., Li, H., Li, X.: Robust discrete spectral hashing for large-scale image semantic indexing. IEEE TBD 1(4), 162–171 (2015)
Zhang, P., Zhang, W., Li, W.-J., Guo, M.: Supervised hashing with latent factor models. In: Proceedings of SIGIR, pp. 173–182 (2014)
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Project 61572108, Project 61632007 and Project 61502081, the National Thousand-Young-Talents Program of China, and the Fundamental Research Funds for the Central Universities under Project ZYGX2014Z007 and Project ZYGX2015J055.
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He, S. et al. (2017). Efficient Supervised Hashing via Exploring Local and Inner Data Structure. In: Huang, Z., Xiao, X., Cao, X. (eds) Databases Theory and Applications. ADC 2017. Lecture Notes in Computer Science(), vol 10538. Springer, Cham. https://doi.org/10.1007/978-3-319-68155-9_8
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DOI: https://doi.org/10.1007/978-3-319-68155-9_8
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