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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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
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.
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.
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