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A knowledge-based approach in video frame processing using Iterative Qualitative Data Analysis

机译:一种基于知识的迭代定性数据分析方法

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The ability to acquire, identify and represent the knowledge that a human expert has about a particular domain is a powerful key method in the development of a knowledge-based computer system. This paper demonstrates a methodology for acquiring and analyzing data based on semi-structured interview responses conducted upon human experts. Human experts are asked to determine the acceptability of an image containing person(s) in a sequence of images. Different experts may have different judgments and collectively the image values or attributes from their subjective judgment may contribute to the main factors of consideration in determining the overall image acceptability. The aim of this paper is to identify the most appropriate image attributes used by human experts during an image selection task which is in line with our research objectives. We discuss the knowledge acquisition task by adopting the Iterative Qualitative Data Analysis (IQDA) approach and represent the knowledge into a set of filtering attributes.
机译:在基于知识的计算机系统的开发中,获得,识别和表示人类专家对特定领域的知识的能力是强大的关键方法。本文展示了一种基于对人类专家进行的半结构化访谈响应来获取和分析数据的方法。要求人类专家确定图像序列中包含人的图像的可接受性。不同的专家可能有不同的判断,并且主观判断得出的图像值或属性可能会共同决定确定整体图像可接受性时要考虑的主要因素。本文的目的是确定人类专家在图像选择任务中使用的最合适的图像属性,这与我们的研究目标是一致的。我们通过采用迭代定性数据分析(IQDA)方法讨论知识获取任务,并将知识表示为一组过滤属性。

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