Novelty Detection Using Graphical Models for Semantic Room Classification
Novelty Detection Using Graphical Models for Semantic Room Classification
复制标题
使用图形模型进行语义房间分类的新颖性检测
DOI：
10.1007/978-3-642-24769-9_24
复制
发表时间：
2011
期刊：
影响因子：
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通讯作者：
Luis Paulo Reis
中科院分区：
文献类型：
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作者：
André Susano Pinto;Andrzej Pronobis;Luis Paulo Reis
本文提出了一种基于语义房间分类的新颖性检测方法。为环境中的区域分配语义标签的能力对于旨在执行复杂的类人任务和人类交互的自主代理至关重要。然而，为了鲁棒性和自然地从人类用户那里学习语义，代理必须能够识别其自身知识中的空白。为此，我们提出了一种基于图形模型的方法来识别与先前学习的任何语义描述不匹配的新输入。该方法采用根据条件概率和无条件概率定义的新颖性阈值。然后使用从未标记数据训练的无条件概率密度模型优化新颖性阈值。
This paper presents an approach to the problem of novelty detection in the context of semantic room categorization. The ability to assign semantic labels to areas in the environment is crucial for autonomous agents aiming to perform complex human-like tasks and human interaction. However, in order to be robust and naturally learn the semantics from the human user, the agent must be able to identify gaps in its own knowledge. To this end, we propose a method based on graphical models to identify novel input which does not match any of the previously learnt semantic descriptions. The method employs a novelty threshold defined in terms of conditional and unconditional probabilities. The novelty threshold is then optimized using an unconditional probability density model trained from unlabelled data.