Abstract
Hashing for large scale image retrieval has become more and more popular because of its improvement in computational speed and storage reduction. Spectral Hashing (SH) is a very efficient unsupervised hashing method through mapping similar images to similar binary codes. However, it doesn’t take the non-neighbor points into consideration, and its assumption of uniform data distribution is usually not true. In this paper, we propose a \(local\ linear\ spectral\ hashing\) framework that minimizes the average Hamming distance with a new local neighbor matrix, which can guarantee the mapping not only from neighbor images to neighbor codes, but also from non-neighbor images to non-neighbor codes. Based on the framework, we utilize three linear methods to handle the proposed problem, including orthogonal hashing, non-orthogonal hashing, and sequential hashing. The experiments on two huge datasets demonstrate the efficiency and accuracy of our methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Silpa-Anan, C., Hartley, R.: Optimised kd-trees for fast image descriptor matching. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)
Uhlmann, J.K.: Satisfying general proximity/similarity queries with metric trees. Information Processing Letters 40(4), 175–179 (1991)
Gionis, A., Indyk, P., Motwani, R., et al.: Similarity search in high dimensions via hashing. In: Proceedings of the International Conference on Very Large Data Bases, pp. 518–529 (1999)
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)
Kulis, B., Grauman, K.: Kernelized locality-sensitive hashing for scalable image search. In: IEEE 12th International Conference on Computer Vision, pp. 2130–2137 (2009)
Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. In: Advances in Neural Information Processing Systems, pp. 1753–1760 (2008)
Wang, J., Kumar, S., Chang, S.: Semi-supervised hashing for large scale search. IEEE Trans. on Pattern Analysis and Machine Intelligence 34(12), 2393–2406 (2012)
Salakhutdinov, R., Hinton, G.: Learning a nonlinear embedding by preserving class neighbourhood structure. In: AI and Statistics, vol. 3, p. 5 (2007)
Kulis, B., Jain, P., Grauman, K.: Fast similarity search for learned metrics. IEEE Trans. on Pattern Analysis and Machine Intelligence 31(12), 2143–2157 (2009)
Fowlkes, C., Belongie, S., Chung, F., Malik, J.: Spectral grouping using the nystrom method. IEEE Trans. on Pattern Analysis and Machine Intelligence 26(2), 214–225 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhao, K., Liu, D., Lu, H. (2013). Local Linear Spectral Hashing. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8228. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42051-1_36
Download citation
DOI: https://doi.org/10.1007/978-3-642-42051-1_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42050-4
Online ISBN: 978-3-642-42051-1
eBook Packages: Computer ScienceComputer Science (R0)