摘要翻译：
纹理图像的分类假定是根据具有相同纹理的区域来考虑图像。在不确定的环境中，采取不精确的决策或拒绝一个未学习类所对应的区域可能会更好。而且，在作为分类单元的区域上，我们可以有不止一个纹理。这些考虑允许我们开发一个信念决策模型，允许拒绝一个区域作为未学习，并决定学习类的联合和交叉。该方法在声纳图像海底特征的应用中找到了所有的合理性，并提供了一个例子。
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英文标题：
《Belief decision support and reject for textured images characterization》
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作者：
Arnaud Martin (E3I2)
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最新提交年份：
2008
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分类信息：

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：
  The textured images' classification assumes to consider the images in terms of area with the same texture. In uncertain environment, it could be better to take an imprecise decision or to reject the area corresponding to an unlearning class. Moreover, on the areas that are the classification units, we can have more than one texture. These considerations allows us to develop a belief decision model permitting to reject an area as unlearning and to decide on unions and intersections of learning classes. The proposed approach finds all its justification in an application of seabed characterization from sonar images, which contributes to an illustration.
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PDF链接：
https://arxiv.org/pdf/0807.0627