场景与对象:两种基于上下文的识别方法的比较研究

Andrew Rabinovich, Serge J. Belongie
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引用次数: 13

摘要

上下文模型在目标识别中起着非常重要的作用。多年来,出现了两种类型的上下文模型:基于场景统计总结的上下文推断模型(我们将其称为基于场景的上下文模型,或SBC),以及根据图像中对象之间的关系表示上下文的模型(基于对象的上下文,或OBC)。在设计目标识别系统时,有必要了解这些方法的理论和实际特性。这项工作提供了对这些模型的分析,并使用LabelMe数据集评估了它们的两个代表。我们展示了使用OBC风格的方法有相当大的改进余地。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Scenes vs. objects: A comparative study of two approaches to context based recognition
Contextual models play a very important role in the task of object recognition. Over the years, two kinds of contextual models have emerged: models with contextual inference based on the statistical summary of the scene (we will refer to these as scene based context models, or SBC), and models representing the context in terms of relationships among objects in the image (object based context, or OBC). In designing object recognition systems, it is necessary to understand the theoretical and practical properties of such approaches. This work provides an analysis of these models and evaluates two of their representatives using the LabelMe dataset. We demonstrate a considerable margin of improvement using the OBC style approach.
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