Sparse Spectral Hashing for Content-Based Image Retrieval
International Journal of Intelligent Information Systems
Volume 4, Issue 2-2, March 2015, Pages: 1-4
Received: Jan. 7, 2015; Accepted: Jan. 10, 2015; Published: Feb. 13, 2015
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Authors
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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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.
Keywords
Hashing Index, Sparse Dimensionality Reduction, Laplacian Image
To cite this article
Li Jun-yi, Li Jian-hua, Sparse Spectral Hashing for Content-Based Image Retrieval, International Journal of Intelligent Information Systems. Special Issue: Content-based Image Retrieval and Machine Learning. Vol. 4, No. 2-2, 2015, pp. 1-4. doi: 10.11648/j.ijiis.s.2015040202.11
References
[1]
M. Belkin and P. Niyogi. Towards a theoretical foundation for Laplacian-based manifold methods. Journal of Computer and System Sciences, 74(8):1289 - 1308, 2008.
[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.
[9]
G. Shakhnarovich, P. Viola, and T. Darrell. Fast pose estimation with parameter-sensitive hashing. In Proceedings of IEEE International Conference on Computer Vision (ICCV), page 750, 2003.
[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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