主动分类理论及其在水下检测中的应用

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摘要翻译：
我们讨论了自动驾驶汽车必须基于多个视图对一个目标进行分类的问题。我们关注于活动分类设置，在该设置中，车辆控制选择哪些视图来最好地执行分类。这个问题被描述为贝叶斯主动学习的扩展，我们展示了与这一领域最近的理论保证的联系。我们正式分析了当新信息变得可用时自适应行动的好处。通过分析，提出了一种基于信息论代价的确定最佳观测视图的概率算法。我们从两个方面验证了我们的方法，这两个方面都与水下检测有关：合成深度图中的三维多面体识别和成像声纳船体检测。这些任务包括主动分类问题的规划和识别方面。结果表明，与被动方法相比，主动规划信息视图可以减少80%的必要视图。
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英文标题：
《Active Classification: Theory and Application to Underwater Inspection》
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作者：
Geoffrey A. Hollinger, Urbashi Mitra, Gaurav S. Sukhatme
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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：Robotics        机器人学
分类描述：Roughly includes material in ACM Subject Class I.2.9.
大致包括ACM科目I.2.9类的材料。
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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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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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英文摘要：
  We discuss the problem in which an autonomous vehicle must classify an object based on multiple views. We focus on the active classification setting, where the vehicle controls which views to select to best perform the classification. The problem is formulated as an extension to Bayesian active learning, and we show connections to recent theoretical guarantees in this area. We formally analyze the benefit of acting adaptively as new information becomes available. The analysis leads to a probabilistic algorithm for determining the best views to observe based on information theoretic costs. We validate our approach in two ways, both related to underwater inspection: 3D polyhedra recognition in synthetic depth maps and ship hull inspection with imaging sonar. These tasks encompass both the planning and recognition aspects of the active classification problem. The results demonstrate that actively planning for informative views can reduce the number of necessary views by up to 80% when compared to passive methods.
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PDF链接：
https://arxiv.org/pdf/1106.5829

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