CASIA OpenIR  > 毕业生  > 硕士学位论文
纹理分析与模式分类
其他题名Texture Analysis and Pattern Classification
张凯
学位类型工学硕士
导师卢汉清 ; 唐明
2004-07-01
学位授予单位中国科学院研究生院
学位授予地点中国科学院自动化研究所
学位专业模式识别与智能系统
关键词图象分割 纹理分析 Gobor滤波器 聚类算法 均值移动 非参数估计 联合域分析 Texture Analysis/segmentation Gabor Filter Clustering Non-parametric Density Estimation Image Segentation Mean Shift Joint Domai
摘要特征提取与模式分类是模式识别中的两个基本问题,也是本论文工作的主要线索。论 文从纹理特征提取和纹理图象分割入手,提出了一种基于最优特征空间类间可分离性的滤 波器组设计准则,之后重点讨论了基于核密度估计的非监督聚类方法一均值移动算法。我们 还全面分析和完善了动态均值移动算法,并提出了特征空间重采样策略,从而有效提高了 均值移动算法的分类速度。论文的主要工作包括: ①研究了纹理图象频域特征的形成和提取,在此基础上提出了一系列滤波器参数设计 的准则,目的是在Fisher准则的指导下构造具有尽量好的类间可分性、相关性小, 维数较低的特征空间。实验结果表明,给出的滤波器组参数选择标准在保持较好分 割效果的情况下,能够明显降低分类器设计的复杂度和分类过程的计算量,从而提 高纹理图象分割的速度。 ②研究了均值移动聚类算法的一些基本问题。(1))分析了现有的带宽选择方法对于样 收敛的影响,并提出了一种具有更高优化精度和适应性的混合带宽选择方法。(2) 严格证明了在采用二次核函数时,样本均值移动的经典优化本质-牛顿法寻优。并 对[51]中的“blurring process”进行了深入的分析,在此基础上提出了完整的动态均 值移动算法,包括新的带宽选择方法和收敛停止准则。通过对样本集合的动态更新 和更有效的叠代停止标准的设计,动态算法的样本收敛速度比静态均值移动有了显 著提高。本文对高斯分布情况下静态与动态均值移动算法的样本收敛速度进行了理 论上的分析和比较。实际样本分类和图象分割实验表明,动态均移动算法在保持了 良好分类效果的前提下明显的提高了算法速度。 ③将动态均值移动算法结合图象空间信息,采用局部的特征空间聚类来完成图象分 割,并与基于联合域分析的静态均值动算法做了比较。本章还提出了均值移动的快 速算法一基于特征空间重新采样的均值移动算法。通过把原样本集合分解成一系列 样本子集,用样本子集中心和集合大小作为样本分布的近似描述,极大的降低了均 值移动算法的复杂度,同时保持了满意的分类精度。图象分割实验表明,重采样策 略将均值移动算法速度提高了两个数量级,并保持了满意的分割效果,是一种非常 实用的彩色图象分割算法
其他摘要Feature extraction and pattern classification are two basic yet quite important aspects of Pattern Recognition, which outline this thesis. We first discuss the problem of texture image segmentation, and propose a series of principles for filter bank selection to obtain good class separability in the feature space. Then we focus on mean shift, an unsupervised clustering algorithm derived from kernel density estimation. We comprehensively analyze the dynamic mean shift algorithm, and propose the feature space re-sampling scheme for fast mean shift implementation. The primary work of the paper includes: ①We analyze the energy distribution of textures in the frequency domain: its formation mechanism and distribution character, on which a series of adaptive filter bank designing principles are proposed. Orientated to the optimal inter-class reparability, the buildup of the feature space is directly guided by Fisher discriminant criterion, which provides the extracted features with good reparability, low dimensionality and small correlation. With the proposed scheme, satisfactory segmentation result is obtained and the calculation is greatly reduced on the compact filter bank with moderately low dimensions. ②We make further analysis on some basic problems of the iterative clustering algorithm- Mean Shift. (1) We examine the influence of bandwidth selection on sample convergence behavior, and propose a hybrid bandwidth selection scheme which better adapts to sample distribution and achieves higher optimization precision. (2) We strictly prove that the mean shift convergence using Epanechnikov kernel is equivalent to adopting Newton optimization method to locate the local maximum of the density estimated with the same kernel. We also develop a complete Dynamic Mean Shift clustering algorithm based on the deep analysis of the "blurring process" in [51], including the robust bandwidth selection and a new stop criterion. Compared with static mean shift, the convergence of dynamic one is greatly accelerated by the dynamic update of samples and the novelty designed stopping criterion. Theoretical comparisons on convergence between the two algorithms in Gaussian distribution cases, as well as practical clustering and segmentation experiments both prove the efficiency of dynamic mean shift algorithm. ③We combine dynamic mean shift with the spatial information of the image for segmentation tasks, and compare it with static mean shift employed in the joint spatial-range domain. We also develop a feature space re-samplingscheme for fast mean shift implementation. By decomposing the original data set into a series of local sample set with corresponding distribution parameter, the mean shift procedure can be applied in the newly created feature space with much lower computational complexity, therefore greatly speeding up the algorithm. Color image segmentation experiments show that segmentation speed is increased significan
馆藏号XWLW793
其他标识符793
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/6773
专题毕业生_硕士学位论文
推荐引用方式
GB/T 7714
张凯. 纹理分析与模式分类[D]. 中国科学院自动化研究所. 中国科学院研究生院,2004.
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