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中文摘要：
针对半调图像分类问题，提出黎曼流形上的协方差建模方法和贝叶斯分类策略．根据半调图像傅立叶频谱的特点，提出一种基于模板矩阵的特征获取方法，并结合频谱信息形成协方差矩阵描述方法．通过引入有效图像判决规则和分块技术，提出一种协方差矩阵提取算法．利用样本的局部特性和核密度估计方法，实现黎曼流形上的贝叶斯分类策略．实验中研究阈值参数的选择策略，与5个相似方法进行分类性能比较，探讨有关参数对性能的影响．实验结果表明，所提出的方法在Q=32或64和L=10～15时其分类错误率低于4％，建模时间开销低于100ms，且优于5个相似方法．
英文摘要：
A covariance modeling method and a Bayesian method on Riemannian manifold are presented for classification of halftone image. According to the Fourier spectrum characteristic of halftone image, a feature extraction based on template matrices is ＇presented to form a covariance matrix by combining with the spectrum of halftone image. An algorithm for covariance matrix extraction of halftone image is proposed by introducing a decision rule of effective image and partitioning technology. A Bayesian rule based on neighbor characteristic of tested samples and kernel density estimation is presented on Riemannian manifolds of symmetric positive definite matrices. In experiments, the problem of selection on threshold parameter is studied by statistical methods, the comparisons of the proposed method with 5 similar methods are conducted, and the influences of two parameters on classification performance and time cost of feature modeling are discussed. The experimental results show that the classification error of the proposed method is below 4% and computation time of modeling is under lOOms if parameters Q = 32 or 64 and L= 10 - 15. Furthermore, the proposed method is superior to other 5 methods.
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