Learning multi-label scene classification
Learning multi-label scene classification
复制标题
DOI：
10.1016/j.patcog.2004.03.009
复制
发表时间：
2004-09-01
影响因子：
8
通讯作者：
Brown, CM
中科院分区：
文献类型：
--
作者：
Boutell, MR;Luo, JB;Brown, CM
在经典的模式识别问题中，根据定义，类是互斥的。当类别在特征空间中重叠时，就会出现分类错误。我们研究了一种不同的情况，根据定义，当这些类不互相排斥时，就会发生这种情况。此类问题出现在语义场景和文档分类以及医学诊断中。我们提出了一个框架来处理此类问题并将其应用于语义场景分类问题，其中自然场景可能包含多个对象，使得场景可以通过多个类标签来描述（例如，背景中有山的田野场景）。这样的问题对经典模式识别范式提出了挑战，需要不同的处理方法。我们讨论了这种情况下的训练和测试方法，并引入了用于评估单个示例、类召回率和精度以及总体准确性的新指标。实验表明我们的方法适用于场景分类；此外，我们的工作似乎可以推广到相同性质的其他分类问题。 (C) 2004 年模式识别协会。由爱思唯尔有限公司出版。保留所有权利。
In classic pattern recognition problems, classes are mutually exclusive by definition. Classification errors occur when the classes overlap in the feature space. We examine a different situation, occurring when the classes are, by definition, not mutually exclusive. Such problems arise in semantic scene and document classification and in medical diagnosis. We present a framework to handle such problems and apply it to the problem of semantic scene classification, where a natural scene may contain multiple objects such that the scene can be described by multiple class labels (e.g., a field scene with a mountain in the background). Such a problem poses challenges to the classic pattern recognition paradigm and demands a different treatment. We discuss approaches for training and testing in this scenario and introduce new metrics for evaluating individual examples, class recall and precision, and overall accuracy. Experiments show that our methods are suitable for scene classification; furthermore, our work appears to generalize to other classification problems of the same nature. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.