摘要翻译：
本文提出了一种新的评价方法，用于不确定环境下纹理图像的分类和分割。在不确定的环境中，真正的类和边界是已知的，只有专家给出的部分确定性。大多数时候，在许多已发表的论文中，只考虑分类或只考虑分割，并对其进行评价。在这里，我们建议根据专家给出的确定性来兼顾分类和分割结果。我们给出了该方法在海底特征声纳图像分类器融合上的结果。
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英文标题：
《Fusion for Evaluation of Image Classification in Uncertain Environments》
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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：
  We present in this article a new evaluation method for classification and segmentation of textured images in uncertain environments. In uncertain environments, real classes and boundaries are known with only a partial certainty given by the experts. Most of the time, in many presented papers, only classification or only segmentation are considered and evaluated. Here, we propose to take into account both the classification and segmentation results according to the certainty given by the experts. We present the results of this method on a fusion of classifiers of sonar images for a seabed characterization.
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PDF链接：
https://arxiv.org/pdf/0805.3935