MEMBER, IEEE, Department of Electrical Engineering, College of Engineering, Louisiana State University, Baton Rouge, LA 70803.
IEEE Trans Pattern Anal Mach Intell. 1980 Mar;2(3):204-22. doi: 10.1109/tpami.1980.4767008.
An evaluation of the ability of four texture analysis algorithms to perform automatic texture discrimination will be described. The algorithms which will be examined are the spatial gray level dependence method (SGLDM), the gray level run length method (GLRLM), the gray level difference method (GLDM), and the power spectral method (PSM). The evaluation procedure employed does not depend on the set of features used with each algorithm or the pattern recognition scheme. Rather, what is examined is the amount of texturecontext information contained in the spatial gray level dependence matrices, the gray level run length matrices, the gray level difference density functions, and the power spectrum. The comparison will be performed in two steps. First, only Markov generated textures will be considered. The Markov textures employed are similar to the ones used by perceptual psychologist B. Julesz in his investigations of human texture perception. These Markov textures provide a convenient mechanism for generating certain example texture pairs which are important in the analysis process. In the second part of the analysis the results obtained by considering only Markov textures will be extended to all textures which can be represented by translation stationary random fields of order two. This generalization clearly includes a much broader class of textures than Markovian ones. The results obtained indicate that the SGLDM is the most powerful algorithm of the four considered, and that the GLDM is more powerful than the PSM.
将描述一种评估四种纹理分析算法进行自动纹理区分能力的方法。所检查的算法是空间灰度依赖方法（SGLDM）、灰度运行长度方法（GLRLM）、灰度差分方法（GLDM）和功率谱方法（PSM）。所采用的评估过程不依赖于每种算法使用的特征集或模式识别方案。相反，检查的是空间灰度依赖矩阵、灰度运行长度矩阵、灰度差分密度函数和功率谱中包含的纹理上下文信息量。比较将分两步进行。首先，仅考虑马尔可夫生成的纹理。所采用的马尔可夫纹理类似于感知心理学家 B. Julesz 在他的人类纹理感知研究中使用的纹理。这些马尔可夫纹理为生成在分析过程中重要的某些示例纹理对提供了一种方便的机制。在分析的第二部分，仅考虑马尔可夫纹理获得的结果将扩展到可以用二阶平移平稳随机域表示的所有纹理。这种推广显然包括比马尔可夫纹理更广泛的纹理类。所得结果表明，SGLDM 是四种考虑的算法中最强大的算法，GLDM 比 PSM 更强大。
