Abstract
Recent years have witnessed the effectiveness and efficiency of learning-based hashing methods which generate short binary codes preserving the Euclidean similarity in the original space of high dimension. However, because of their complexities and out-of-sample problems, most of methods are not appropriate for embedding of large-scale datasets. In this paper, we have proposed a new supervised hashing method to generate class-specific hash codes, which uses an inductive process based on the Inductive Manifold Hashing (IMH) model and leverage supervised information into hash codes generation to address these difficulties and boost the hashing quality. It is experimentally shown that this method gets excellent performance of image classification and retrieval on large-scale multimedia dataset just with very short binary codes.
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References
Wang, J., Kumar, S., Chang, S.F.: Semi-supervised hashing for scalable image retrieval. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3424–3431. IEEE (2010)
Yang, Y., Zha, Z.J., Gao, Y., et al.: Exploiting web images for semantic video indexing via robust sample-specific loss. Multimedia, IEEE Trans. 16(6), 1677–1689 (2014)
Datar, M., Immorlica, N., Indyk, P., et al.: Locality-sensitive hashing scheme based on p-stable distributions. In: Proceedings of the Twentieth Annual Symposium on Computational Geometry, pp. 253–262. ACM (2004)
Kulis, B., Jain, P., Grauman, K.: Fast similarity search for learned metrics. Pattern Anal. Mach. Intell. IEEE Trans. 31(12), 2143–2157 (2009)
Kulis, B., Grauman, K.: Kernelized locality-sensitive hashing for scalable image search. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 2130–2137. IEEE (2009)
Raginsky, M., Lazebnik, S.: Locality-sensitive binary codes from shift-invariant kernels. In: Advances in Neural Information Processing Systems, pp. 1509–1517 (2009)
Wang, J., Kumar, S., Chang, S.F.: Semi-supervised hashing for large-scale search. Pattern Anal. Mach. Intell. IEEE Trans. 34(12), 2393–2406 (2012)
Gong, Y., Lazebnik, S.: Iterative quantization: A procrustean approach to learning binary codes. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 817–824. IEEE (2011)
Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. In: Advances in Neural Information Processing Systems, pp. 1753–1760 (2009)
Liu, W., Wang, J., Kumar, S., et al.: Hashing with graphs. In: Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp. 1–8 (2011)
Shen, F., Shen, C., Shi, Q., van den Hengel, A., Tang, Z., Shen, H.T.: Hashing on nonlinear manifolds. Image Process. IEEE Trans. 24(6), 1839–1851 (2015)
Shen, F., Shen, C., Shi, Q., et al.: Inductive hashing on manifolds. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1562–1569. IEEE (2013)
Yang, Y., Yang, Y., Huang, Z., et al.: Tag localization with spatial correlations and joint group sparsity. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 881–888. IEEE (2011)
Strecha, C., Bronstein, A.M., Bronstein, M.M., et al.: LDAHash: Improved matching with smaller descriptors. Pattern Anal. Mach. Intell. IEEE Trans. 34(1), 66–78 (2012)
Kulis, B., Darrell, T.: Learning to hash with binary reconstructive embeddings. In: Advances in Neural Information Processing Systems, pp. 1042–1050 (2009)
Liu, W., Wang, J., Ji, R., et al.: Supervised hashing with kernels. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2074–2081. IEEE (2012)
Lin, G., Shen, C., Suter, D., et al.: A general two-step approach to learning-based hashing. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 2552–2559. IEEE (2013)
Wang, J., Kumar, S., Chang, S.F.: Semi-supervised hashing for scalable image retrieval. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3424–3431. IEEE (2010)
Lin, G., Shen, C., Shi, Q., et al.: Fast supervised hashing with decision trees for high-dimensional data. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1971–1978. IEEE (2014)
Norouzi, M., Blei, D.M.: Minimal loss hashing for compact binary codes. In: Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp. 353–360 (2011)
Shen, F., Shen, C., Liu, W., et al.: Supervised discrete hashing. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 37–45 (2015)
Rohrbach, M., Stark, M., Szarvas, G., et al.: What helps where–and why? semantic relatedness for knowledge transfer. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 910–917. IEEE (2010)
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Zhang, M., Shen, F., Zhang, H., Xie, N., Yang, W. (2015). Hashing with Inductive Supervised Learning. In: Ho, YS., Sang, J., Ro, Y., Kim, J., Wu, F. (eds) Advances in Multimedia Information Processing -- PCM 2015. PCM 2015. Lecture Notes in Computer Science(), vol 9315. Springer, Cham. https://doi.org/10.1007/978-3-319-24078-7_45
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DOI: https://doi.org/10.1007/978-3-319-24078-7_45
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