Application of Image Recognition Based on Grey Relational Analysis
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pplication of Image Recognition Based on Grey Relational Analysis Hua Li* Electronic Information and Electrical Engineering College Shangluo University, Shangluo, Shaanxi, 726000 China *e-mail: [email protected] Received November 12, 2019; revised February 21, 2020; accepted March 13, 2020
Abstract—With the rapid development of society, the requirements and standards of image recognition are getting higher and higher. Therefore, applying some new technologies to image recognition has become an extremely important research topic. In this study, based on the grey relational analysis method, the face image was taken as the main research subject, an image recognition model based on the grey relational analysis method was established, and the related experimental results were obtained. Compared with the traditional face image recognition system, the image recognition method based on the grey relational analysis method had higher recognition speed and good recognition performance. This study provides a new path for the research of image recognition. Keywords: grey relational analysis, face image, recognition, features DOI: 10.3103/S0146411620040070
1. INTRODUCTION With the rapid development of science and technology, the application of image recognition has become an important research topic [1]. Image recognition mainly refers to recognizing objects in various patterns by using computer platform for the analysis and understanding of the required images. It plays a vital role in various fields and provides great convenience for people’s life and work. Nowadays, based on some new technologies [2], such as grey relational analysis, computer vision, machine vision and neural network image recognition technology, we can study image recognition conveniently and effectively, improve and optimize the mechanism of image recognition, further enhance the performance of image recognition, and speed up the efficiency of image recognition [3]. In response to this problem, many experts and scholars have put forward their own views. Qiao et al. [4] proposed a grey image edge adaptive algorithm based on grey relational analysis which could obtain more complete edge images and had better continuity. Ding et al. [5] proposed an image matching method based on grey relational degree and feature point analysis and verified that the algorithm with high robustness could meet the requirements of image recognition under various conditions and eliminate the influence. Thepade et al. [6] proposed new content-based image recognition and feature extraction technology based on block truncation coding and found that the minimum value of precision of classification and searching by using the technology improved compared to the current technologies. Wang et al. [7] built, implemented and tested a Drosophila image recognition system based on computer vision. The system used Gabor surface features in automatic recognition. Through independent multi-part image automatic recognition test, the overall classification success rate of species level reached 87%.
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