吉林大学学报(理学版) ›› 2019, Vol. 57 ›› Issue (06): 1437-1441.

• 计算机科学 • 上一篇    下一篇

基于改进局部二值模式和梯度特征的计算机生成图像鉴别算法

袁哲1, 孙延君2, 陈亮1   

  1. 1. 吉林大学 机械与航空航天工程学院, 长春 130022; 2. 空军航空大学 计算机教研室, 长春 130022
  • 收稿日期:2019-06-19 出版日期:2019-11-26 发布日期:2019-11-21
  • 通讯作者: 袁哲 E-mail:yuanzhe@jlu.edu.cn

ComputerGenerated Image Recognition Algorithm Based onImproved Local Binary Pattern and Gradient Features

YUAN Zhe1, SUN Yanjun2, CHEN Liang1   

  1. 1. College of Mechanical and Aerospace Engineering, Jilin University, Changchun 130022, China;2. Department of Computer, Air Force Aviation University, Changchun 130022, China
  • Received:2019-06-19 Online:2019-11-26 Published:2019-11-21
  • Contact: YUAN Zhe E-mail:yuanzhe@jlu.edu.cn

摘要: 针对目前计算机生成图像鉴别算法存在的计算复杂度高及检测率低等问题, 提出一种改进局部二值模式和梯度特征的计算机生成图像鉴别算法. 该算法主要基于图像的局部纹理特征, 先提取计算机生成图像和自然图像的特征向量, 再将该特征利用SVM分类器进行分类. 实验结果表明, 该算法可有效地鉴别计算机生成图像和真实图像.

关键词: 图像处理, 局部二值模式, 图像梯度, 图像鉴别算法, 粒子图像测速技术

Abstract: Aiming at the problems of high computational complexity and low detection rate of current computergenerated image recognition algorithm, we proposed a computergenerated image recognition algorithm based on improved local binary pattern (LBP) and gradient feature. The improved algorithm was mainly based on the local texture feature of the image. Firstly, the feature vectors of the computergenerated images and the natural images were extracted, and then these features were classified by SVM classifier. The experimental results show that the proposed algorithm can effectively discriminate computergenerated images and real images.

Key words: image processing, local binary pattern, image gradient,  , image recognition algorithm, particle image velocimetry technology

中图分类号: 

  • TP309