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Mathematical Problems in Engineering
Volume 2018, Article ID 1917421, 9 pages
Research Article

An Image Similarity Acceleration Detection Algorithm Based on Sparse Coding

1Department of Mathematics and Computer Science, Changsha University, Changsha 410003, China
2College of Systems Engineering, National University of Defense Technology, Changsha 410073, China

Correspondence should be addressed to Xie Yuxiang; nc.ude.tdun@eixxy

Received 2 January 2018; Accepted 25 March 2018; Published 20 May 2018

Academic Editor: Xosé M. Pardo

Copyright © 2018 Luan Xidao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Aiming at the problem that the image similarity detection efficiency is low based on local feature, an algorithm called ScSIFT for image similarity acceleration detection based on sparse coding is proposed. The algorithm improves the image similarity matching speed by sparse coding and indexing the extracted local features. Firstly, the SIFT feature of the image is extracted as a training sample to complete the overcomplete dictionary, and a set of overcomplete bases is obtained. The SIFT feature vector of the image is sparse-coded with the overcomplete dictionary, and the sparse feature vector is used to build an index. The image similarity detection result is obtained by comparing the sparse coefficients. The experimental results show that the proposed algorithm can significantly improve the detection speed compared with the traditional algorithm based on local feature detection under the premise of guaranteeing the accuracy of algorithm detection.