Discrimination of natural images and computer generated graphics based on multi-fractal and regression analysis
Discrimination of natural images and computer generated graphics based on multi-fractal and regression analysis
复制标题
基于多重分形和回归分析的自然图像和计算机生成图形的区分
DOI：
10.1016/j.aeue.2016.11.009
复制
发表时间：
2017-01-01
影响因子：
3.2
通讯作者：
Sun, Xing-ming
中科院分区：
文献类型：
--
作者：
Peng, Fei;Zhou, Die-lan;Sun, Xing-ming
本文工作的目的是区分自然图像(NI)和计算机生成图形(CG)。分析了NI和CG残差图像的纹理差异。首先利用多元线性回归提取残差图像，然后研究回归模型的拟合度。通过分析它们残差图像的差异，提取了9维的直方图特征和9维的多重分形谱特征来表示它们的纹理差异。利用支持向量机分类器，结合回归模型的6维适应度特征，区分自然图像和计算机生成的图形。实验结果和分析表明，该算法的平均识别正确率为98.69%，对JPEG压缩、旋转、加性噪声和图像缩放等具有较强的鲁棒性。与已有的一些方法相比，特征的选择是有效的，并且需要较少的特征来表示NI和CG之间的差异。同时，显著减少了分类时间，并保持了稳健性。该方法在图像源管道识别中具有很大的应用潜力。(C)2016年爱思唯尔股份有限公司。全。版权保留。
The aim of the work presented in this paper is to discriminate natural images (NI) and computer generated graphics (CG). The texture differences are analyzed to the residual images of NI and CG. The residual images are first extracted by using multiple linear regressions, and then the fitting degree of the regression model is investigated. Through the analysis of the difference of their residual images, 9 dimensions,of histogram features and 9 dimensions of multi-fractal spectrum features are extracted to represent their texture differences. Combined with 6 dimensions of regression model fitness features, natural images and computer generated graphics are discriminated by using a support vector machine (SVM) classifier. Experimental results and analysis show that it can achieve an average identification accuracy of 98.69%, and it is robust against JPEG compression, rotation, additive noise and image resizing. Compared with some existed methods, the selection of features is effective and fewer features are required for representing the differences between NI and CG. Meanwhile, the classification time is significantly reduced and the robustness is maintained. It has great potential to be used in image source pipeline identification. (C) 2016 Elsevier GmbH. All. rights reserved.