不确定图像分类与分割的评价

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摘要翻译：
每年都有大量的分割和分类算法被发明或重用来解决需要机器视觉的问题。通常，这些算法的效率与一个或多个人类专家给出的结果进行比较。然而，在许多情况下，对象的真实边界的位置以及它们的类别并不是由人类专家确定的。而且，一般只对分割和分类问题的一个方面进行评价。本文提出了一种新的图像分类分割评价方法，该方法综合考虑了图像的分类分割结果和专家给出的确定性水平。作为该方法的一个具体实例，我们评估了一种基于声纳图像的海底自动特征化算法。
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英文标题：
《Evaluation for Uncertain Image Classification and Segmentation》
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作者：
Arnaud Martin (E3I2), Hicham Laanaya (E3I2), Andreas Arnold-Bos (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：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：
  Each year, numerous segmentation and classification algorithms are invented or reused to solve problems where machine vision is needed. Generally, the efficiency of these algorithms is compared against the results given by one or many human experts. However, in many situations, the location of the real boundaries of the objects as well as their classes are not known with certainty by the human experts. Furthermore, only one aspect of the segmentation and classification problem is generally evaluated. In this paper we present a new evaluation method for classification and segmentation of image, where we take into account both the classification and segmentation results as well as the level of certainty given by the experts. As a concrete example of our method, we evaluate an automatic seabed characterization algorithm based on sonar images.
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PDF链接：
https://arxiv.org/pdf/0806.1796

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关键词：不确定 Segmentation Presentation Intelligence Recognition 给出 segmentation certainty 分割 重用