摘要翻译：
自80年代后期以来，纹理分类一直是图像处理领域的研究热点之一。因此，至今已提出了许多不同的方法来解决这个问题。在这些方法中，研究人员试图基于线性和非线性模式来描述和识别纹理。任何窗口上的线性和非线性模式都是基于颗粒成分以特定顺序形成的。谷物成分是一个原始的形态单位，其中最有意义的信息往往以出现的形式出现。本文提出的方法可以根据纹理的纹理成分对纹理进行分析，然后通过制作纹理成分直方图并提取统计特征对纹理进行分类。最后，为了提高分类的准确性，将该方法扩展到彩色图像，利用该方法对每个RGB通道进行单独分析的能力。虽然该方法是一种通用的方法，适用于不同的应用场合，但该方法已经在石材纹理上进行了测试，结果证明了该方法的质量。
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英文标题：
《Color Texture Classification Approach Based on Combination of Primitive
  Pattern Units and Statistical Features》
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作者：
Shervan Fekri Ershad
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最新提交年份：
2011
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Texture classification became one of the problems which has been paid much attention on by image processing scientists since late 80s. Consequently, since now many different methods have been proposed to solve this problem. In most of these methods the researchers attempted to describe and discriminate textures based on linear and non-linear patterns. The linear and non-linear patterns on any window are based on formation of Grain Components in a particular order. Grain component is a primitive unit of morphology that most meaningful information often appears in the form of occurrence of that. The approach which is proposed in this paper could analyze the texture based on its grain components and then by making grain components histogram and extracting statistical features from that would classify the textures. Finally, to increase the accuracy of classification, proposed approach is expanded to color images to utilize the ability of approach in analyzing each RGB channels, individually. Although, this approach is a general one and it could be used in different applications, the method has been tested on the stone texture and the results can prove the quality of approach.
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PDF链接：
https://arxiv.org/pdf/1109.1133