Abstract
Binary code learning techniques have recently been actively studied for hashing based nearest neighbor search in computer vision applications due to its merit of improving hashing performance. Currently, hashing based methods can obtain good binary codes but some data may suffer from the problem of being mapped to inappropriate Hamming codes. To address this issue, this paper proposes a novel binary code learning method via iterative distance adjustment to improve traditional hashing methods, in which we utilize very short additional binary bits to correct the spatial relationship among data points and thus enhance the similarity-preserving power of binary codes. We carry out image retrieval experiments on the well-recognized benchmark datasets to validate the proposed method. The experimental results have shown that the proposed method achieves better hashing performance than the state-of-the-art binary code learning methods.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Datar, M., Immorlica, N., Indyk, P., Mirrokni, V.S.: Locality-sensitive hashing scheme based on p-stable distributions. In: Proceedings of the Twentieth Annual Symposium on Computational Geometry, pp. 253–262. ACM (2004)
Fan, L.: Supervised binary hash code learning with jensen shannon divergence. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 2616–2623. IEEE (2013)
Freund, Y., Haussler, D.: Unsupervised learning of distributions of binary vectors using two layer networks. Computer Research Laboratory, University of California, Santa Cruz (1994)
Friedman, J.H., Bentley, J.L., Finkel, R.A.: An algorithm for finding best matches in logarithmic expected time. ACM Transactions on Mathematical Software (TOMS) 3(3), 209–226 (1977)
Gionis, A., Indyk, P., Motwani, R., et al.: Similarity search in high dimensions via hashing. VLDB 99, 518–529 (1999)
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)
He, K., Wen, F., Sun, J.: K-means hashing: an affinity-preserving quantization method for learning binary compact codes. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2938–2945. IEEE (2013)
Kulis, B., Darrell, T.: Learning to hash with binary reconstructive embeddings. In: Advances in Neural Information Processing Systems, pp. 1042–1050 (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)
Liu, T., Moore, A.W., Yang, K., Gray, A.G.: An investigation of practical approximate nearest neighbor algorithms. In: Advances in Neural Information Processing Systems, pp. 825–832 (2004)
Liu, W., Wang, J., Ji, R., Jiang, Y.G., Chang, S.F.: Supervised hashing with kernels. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2074–2081. IEEE (2012)
Liu, W., Wang, J., Kumar, S., Chang, S.F.: Hashing with graphs. In: Proceedings of the 28th International Conference on Machine Learning (ICML 2011), pp. 1–8 (2011)
Liu, X., He, J., Lang, B., Chang, S.F.: Hash bit selection: a unified solution for selection problems in hashing. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1570–1577. IEEE (2013)
Mu, Y., Shen, J., Yan, S.: Weakly-supervised hashing in kernel space. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3344–3351. IEEE (2010)
Norouzi, M., Blei, D.M.: Minimal loss hashing for compact binary codes. In: Proceedings of the 28th International Conference on Machine Learning (ICML 2011), pp. 353–360 (2011)
Oliva, A., Torralba, A.: Modeling the shape of the scene: A holistic representation of the spatial envelope. International Journal of Computer Vision 42(3), 145–175 (2001)
Salakhutdinov, R., Hinton, G.E.: Learning a nonlinear embedding by preserving class neighbourhood structure. In: International Conference on Artificial Intelligence and Statistics, pp. 412–419 (2007)
Torralba, A., Fergus, R., Freeman, W.T.: 80 million tiny images: A large data set for nonparametric object and scene recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(11), 1958–1970 (2008)
Uhlmann, J.K.: Satisfying general proximity/similarity queries with metric trees. Information Processing Letters 40(4), 175–179 (1991)
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)
Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. In: Advances in neural information processing systems, pp. 1753–1760 (2008)
Zhang, L., Zhang, Y., Tang, J., Lu, K., Tian, Q.: Binary code ranking with weighted hamming distance. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1586–1593. IEEE (2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Ju, Zf., Mao, Xj., Li, N., Yang, Yb. (2015). Binary Code Learning via Iterative Distance Adjustment. In: He, X., Luo, S., Tao, D., Xu, C., Yang, J., Hasan, M.A. (eds) MultiMedia Modeling. MMM 2015. Lecture Notes in Computer Science, vol 8935. Springer, Cham. https://doi.org/10.1007/978-3-319-14445-0_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-14445-0_8
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14444-3
Online ISBN: 978-3-319-14445-0
eBook Packages: Computer ScienceComputer Science (R0)