| 基于LBC的计算机生成图像盲鉴别算法 |
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| 引用本文： | 申铉京,李梦臻,吕颖达,陈海鹏.基于LBC的计算机生成图像盲鉴别算法[J].计算机科学,2015,42(6):135-138, 161. |
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| 作者姓名： | 申铉京 李梦臻 吕颖达 陈海鹏 |
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| 作者单位： | 1. 吉林大学计算机科学与技术学院 长春130012
2. 吉林大学符号计算与知识工程教育部重点实验室 长春130012 |
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| 基金项目： | 本文受国家青年科学基金项目(61305046),吉林省自然科学基金项目(20140101193JC),吉林省青年科学基金项目(20130522117JH)资助 |
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| 摘 要： | 针对现有的计算机生成图像盲鉴别算法选用的分类特征维度较高、通用性差等问题,提出了一种基于局部二进制计数模式的计算机生成图像盲鉴别算法.首先,将原始图像由RGB颜色空间转换为HSV颜色空间;然后,提取HSV颜色空间图像及其下采样图像的局部二进制计数模式矩阵,求取矩阵归一化直方图;最后,将上述直方图作为分类特征送入SVM分类器,实现计算机生成图像的盲鉴别.实验结果表明,该算法可以有效地鉴别自然图像和计算机生成图像,与现有算法相比具有更高的识别率和较低的特征维度.
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| 关 键 词： | 图像盲鉴别 计算机生成图像 下采样图像 局部二进制计数模式 SVM分类器 |
Blind Identification Algorithm of Photorealistic Computer Graphics Based on Local Binary Count |
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| SHEN Xuan-jing,LI Meng-zhen,LV Ying-da and CHEN Hai-peng.Blind Identification Algorithm of Photorealistic Computer Graphics Based on Local Binary Count[J].Computer Science,2015,42(6):135-138, 161. |
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| Authors: | SHEN Xuan-jing LI Meng-zhen LV Ying-da and CHEN Hai-peng |
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| Affiliation: | College of Computer Science and Technology,Jilin University,Changchun 130012,China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education,Jilin University,Changchun 130012,China,College of Computer Science and Technology,Jilin University,Changchun 130012,China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education,Jilin University,Changchun 130012,China,College of Computer Science and Technology,Jilin University,Changchun 130012,China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education,Jilin University,Changchun 130012,China and College of Computer Science and Technology,Jilin University,Changchun 130012,China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education,Jilin University,Changchun 130012,China |
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| Abstract: | Aiming at the problem that the classification features selected by the existing blind identification algorithms of photorealistic computer graphics have high dimensions and poor universalities,this paper put forward a blind identification algorithm of photorealistic computer graphics based on local binary count.First,the original image is converted from RGB color space to HSV color space.Then,the local binary count matrix is extracted from the HSV color space images and its down-sampling image,and the normalized histogram of the matrix is calculated.Finally,the above histogram is sent as classification features into the SVM classifier,implementing the blind identification of photorealistic computer graphics.The experimental results show that the algorithm can effectively identify photographic images and photorealistic computer graphics.Compared with the existing algorithm,it has higher recognition rate and lower feature dimension. |
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| Keywords: | Blind identification Photorealistic computer graphics Down-sampling image Local binary count SVM classifier |
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