An Image Similarity Acceleration Detection Algorithm Based on Sparse Coding

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

doi:10.1155/2018/1917421

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.

Highlights

Image similarity detection is a hot issue in the field of multimedia information processing
Similar images are created by editing transformation; similarity detection accuracy is generally relatively low based on the global features of the image content
Current image similarity detection methods are mainly based on global features or local features

Summary

Introduction

Image similarity detection is a hot issue in the field of multimedia information processing. Because the number of feature points is small, the calculation speed of image content similarity detection based on global feature is usually very fast. Similar images are created by editing transformation; similarity detection accuracy is generally relatively low based on the global features of the image content. (i) Add tags the image, but its 128-dimensional high dimensional feature vectors and the number of feature points detected will usually bring a large computational burden and reduce the efficiency of the algorithm. In the literature [2], dimension of the SIFT feature point is reduced by principal component analysis (PCA), and a PCA-SIFT feature is proposed This feature reduces the SIFT feature vector from 128 degrees to 32 degrees or less, which improves the efficiency of the algorithm. How to detect the content of similar images quickly and effectively is still a problem

Principle of ScSIFT Algorithm

Process of ScSIFT Algorithm

Experimental Results and Analysis

Conclusions