Sparse Spectral Hashing for Content-Based Image Retrieval

Li Jun-yi; Li Jian-hua

doi:doi:10.11648/j.ijiis.s.2015040202.11

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Sparse Spectral Hashing for Content-Based Image Retrieval

Li Jun-yi, Li Jian-hua

Published in International Journal of Intelligent Information Systems (Volume 4, Issue 2-2)

Received: 7 January 2015 Accepted: 10 January 2015 Published: 13 February 2015

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Abstract

In allusion to similarity calculation difficulty caused by high maintenance of image data, this paper introduces sparse principal component algorithm to figure out embedded subspace after dimensionality reduction of image visual words on the basis of traditional spectral hashing image index method so that image high-dimension index results can be explained overall. This method is called sparse spectral hashing index. The experiments demonstrate the method proposed in this paper superior to LSH, RBM and spectral hashing index methods.

Published in	International Journal of Intelligent Information Systems (Volume 4, Issue 2-2) This article belongs to the Special Issue Content-based Image Retrieval and Machine Learning
DOI	10.11648/j.ijiis.s.2015040202.11
Page(s)	1-4
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2015. Published by Science Publishing Group

Keywords

Hashing Index, Sparse Dimensionality Reduction, Laplacian Image

References

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[2]	Y. Bengio, J. Paiement, P. Vincent, O. Delalleau, N. Le Roux, and M. Ouimet. Out-of-sample extensions for Ile, isomap, mds, eigenmaps, and spectral clustering. In Proceedings of Advances in Neural Information Processing Systems (NIPS), page 177, 2004.
[3]	S. Berchtold, C. Bohm, H. Jagadish, H. Kriegel, and J. Sander. Independent quantization: an index compression technique forhigh-dimensional data spaces. In Proceedings of International Conference on Data Engineering (ICDE), pages 577 - 588, 2000.
[4]	A. D, a' rAspremont, L. E. Ghaoui, M. I. Jordan, and G. G. Lanckriet. A direct formulation for sparse PCA using semidefinite programming. Proceedings of Advances in Neural Information Processing Systems (NIPS), 2004.
[5]	M. Datar, N. Immorlica, P. Indyk, and V. Mirrokni. Locality-sensitive hashing scheme based on p-stable distributions. In Proceedings of Annual Symposium on Computational Geometry, pages 253 - 262. ACM, 2004.
[6]	Q. Lv, W. Josephson, Z. Wang, M. Charikar, and K. Li. Multi-probe LSH: efficient indexing for high-dimensional similarity search. In Proceedings of International Conference on Very Large Data Bases (VLDB), pages 950 - 961, 2007.
[7]	R. Salakhutdinov and G. Hinton. Learning a nonlinear embedding by preserving class neighbourhood structure. In AI and Statistics, 2007.
[8]	R. Salakhutdinov and G. Hinton. Semantic hashing. International Journal of Approximate Reasoning, 50(7):969 - 978, 2009.
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[10]	Y. Tao, K. Yi, C. Sheng, and P. Kalnis. Quality and efficiency in high dimensional nearest neighbor search. In Proceedings of International Conference on Management of Data (SIGMOD), pages 563 - 576. ACM, 2009.
[11]	Y. Weiss, A. Torralba, and R. Fergus. Spectral hashing. In Proceedings of Advances in Neural Information Processing Systems (NIPS), pages 1753 - 1760, 2009.
[12]	H. Zou, T. Hastie, and R. Tibshirani. Sparse principal component analysis. Journal of Computational and Graphical Statistics, 15(2):265 - 286, 2006.

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APA Style

Li Jun-yi, Li Jian-hua. (2015). Sparse Spectral Hashing for Content-Based Image Retrieval. International Journal of Intelligent Information Systems, 4(2-2), 1-4. https://doi.org/10.11648/j.ijiis.s.2015040202.11

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ACS Style

Li Jun-yi; Li Jian-hua. Sparse Spectral Hashing for Content-Based Image Retrieval. Int. J. Intell. Inf. Syst. 2015, 4(2-2), 1-4. doi: 10.11648/j.ijiis.s.2015040202.11

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AMA Style

Li Jun-yi, Li Jian-hua. Sparse Spectral Hashing for Content-Based Image Retrieval. Int J Intell Inf Syst. 2015;4(2-2):1-4. doi: 10.11648/j.ijiis.s.2015040202.11

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@article{10.11648/j.ijiis.s.2015040202.11,
  author = {Li Jun-yi and Li Jian-hua},
  title = {Sparse Spectral Hashing for Content-Based Image Retrieval},
  journal = {International Journal of Intelligent Information Systems},
  volume = {4},
  number = {2-2},
  pages = {1-4},
  doi = {10.11648/j.ijiis.s.2015040202.11},
  url = {https://doi.org/10.11648/j.ijiis.s.2015040202.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.s.2015040202.11},
  abstract = {In allusion to similarity calculation difficulty caused by high maintenance of image data, this paper introduces sparse principal component algorithm to figure out embedded subspace after dimensionality reduction of image visual words on the basis of traditional spectral hashing image index method so that image high-dimension index results can be explained overall. This method is called sparse spectral hashing index. The experiments demonstrate the method proposed in this paper superior to LSH, RBM and spectral hashing index methods.},
 year = {2015}
}

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TY  - JOUR
T1  - Sparse Spectral Hashing for Content-Based Image Retrieval
AU  - Li Jun-yi
AU  - Li Jian-hua
Y1  - 2015/02/13
PY  - 2015
N1  - https://doi.org/10.11648/j.ijiis.s.2015040202.11
DO  - 10.11648/j.ijiis.s.2015040202.11
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 1
EP  - 4
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.s.2015040202.11
AB  - In allusion to similarity calculation difficulty caused by high maintenance of image data, this paper introduces sparse principal component algorithm to figure out embedded subspace after dimensionality reduction of image visual words on the basis of traditional spectral hashing image index method so that image high-dimension index results can be explained overall. This method is called sparse spectral hashing index. The experiments demonstrate the method proposed in this paper superior to LSH, RBM and spectral hashing index methods.
VL  - 4
IS  - 2-2
ER  -

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Author Information

Li Jun-yi

School of Electronic Information and Electrical engineering, Shanghai JiaoTong University, Shanghai 200240, China
Li Jian-hua

School of Electronic Information and Electrical engineering, Shanghai JiaoTong University, Shanghai 200240, China

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APA Style

Li Jun-yi, Li Jian-hua. (2015). Sparse Spectral Hashing for Content-Based Image Retrieval. International Journal of Intelligent Information Systems, 4(2-2), 1-4. https://doi.org/10.11648/j.ijiis.s.2015040202.11

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ACS Style

Li Jun-yi; Li Jian-hua. Sparse Spectral Hashing for Content-Based Image Retrieval. Int. J. Intell. Inf. Syst. 2015, 4(2-2), 1-4. doi: 10.11648/j.ijiis.s.2015040202.11

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AMA Style

Li Jun-yi, Li Jian-hua. Sparse Spectral Hashing for Content-Based Image Retrieval. Int J Intell Inf Syst. 2015;4(2-2):1-4. doi: 10.11648/j.ijiis.s.2015040202.11

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@article{10.11648/j.ijiis.s.2015040202.11,
  author = {Li Jun-yi and Li Jian-hua},
  title = {Sparse Spectral Hashing for Content-Based Image Retrieval},
  journal = {International Journal of Intelligent Information Systems},
  volume = {4},
  number = {2-2},
  pages = {1-4},
  doi = {10.11648/j.ijiis.s.2015040202.11},
  url = {https://doi.org/10.11648/j.ijiis.s.2015040202.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.s.2015040202.11},
  abstract = {In allusion to similarity calculation difficulty caused by high maintenance of image data, this paper introduces sparse principal component algorithm to figure out embedded subspace after dimensionality reduction of image visual words on the basis of traditional spectral hashing image index method so that image high-dimension index results can be explained overall. This method is called sparse spectral hashing index. The experiments demonstrate the method proposed in this paper superior to LSH, RBM and spectral hashing index methods.},
 year = {2015}
}

Copy | Download

TY  - JOUR
T1  - Sparse Spectral Hashing for Content-Based Image Retrieval
AU  - Li Jun-yi
AU  - Li Jian-hua
Y1  - 2015/02/13
PY  - 2015
N1  - https://doi.org/10.11648/j.ijiis.s.2015040202.11
DO  - 10.11648/j.ijiis.s.2015040202.11
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 1
EP  - 4
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.s.2015040202.11
AB  - In allusion to similarity calculation difficulty caused by high maintenance of image data, this paper introduces sparse principal component algorithm to figure out embedded subspace after dimensionality reduction of image visual words on the basis of traditional spectral hashing image index method so that image high-dimension index results can be explained overall. This method is called sparse spectral hashing index. The experiments demonstrate the method proposed in this paper superior to LSH, RBM and spectral hashing index methods.
VL  - 4
IS  - 2-2
ER  -

Copy | Download