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基于三阶统计特征的被动图像拼接检测
  • 摘要

    针对被动图像拼接检测问题,提出了一种基于三阶统计特征的检测算法.该算法把图像状态矩阵中三个相邻状态之间的依赖关系建模为条件共生概率矩阵,然后将其作为识别特征输入到支持向量机(SVM)进行分类.由于高阶统计特征维数随着统计阶数的增加而呈指数级增加,为了降低高维特征在分类阶段所引入的高计算复杂度以及避免可能出现的过拟合现象,引入了主成分分析法(PCA)对提取的特征进行降维处理.实验结果显示,条件共生概率矩阵特征在空间域和8×8分块DCT域的检测结果均优于传统的马尔可夫特征和共生矩阵特征;PCA是图像拼接检测的一个有力分析工具,在大幅度降低特征维数的同时能够保持识别率不降低.

  • 作者

    赵旭东  袁野  李生红  王士林  李建华  ZHAO Xu-dong  YUAN Ye  LI Sheng-hong  WANG Shi-lin  LI Jian-hua 

  • 作者单位

    上海交通大学电子信息与电气工程学院,上海,200240/上海对外贸易学院商务信息学院,上海,201620

  • 刊期

    2012年12期 ISTIC PKU

  • 关键词

    图像拼接检测  三阶统计特征  条件共生概率矩阵  主成分分析  支持向量机 

参考文献
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