An Image Similarity Acceleration Detection Algorithm Based on Sparse Coding

Luan Xidao, Xie Yuxiang, Zhang Lili, Zhang Xin, Li Chen and He Jingmeng

Mathematical Problems in Engineering, 2018, vol. 2018, 1-9

Abstract:

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.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:1917421

DOI: 10.1155/2018/1917421

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