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An adaptive fuzzy approach for modeling visual texture properties

机译:用于建模视觉纹理特性的自适应模糊方法

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The analysis of the perceptual properties of texture plays a fundamental role in tasks like semantic description of images, content-based image retrieval using linguistic queries, or expert systems design based on low level visual features. The presence of these properties in images is very difficult to characterize due to their imprecision, and, moreover, because their perception may change depending on the user or the image context. In this paper, texture properties are modeled by means of an adaptive fuzzy approach that takes into account the subjectivity of the human perception. For this purpose, a methodology in two phases has been proposed. First, non-adaptive fuzzy models, that represent the average human perception about the presence of the texture properties, are obtained. For this modeling, we propose to learn a relationship between representative measures of the properties and the assessments given by human subjects. In a second phase, the obtained fuzzy sets are adapted in order to model the particular perception of the properties that a user may have, as well as the changes in perception influenced by the image context. For this purpose, the membership functions are automatically transformed on the basic of the information given by the user or extracted from the image context, respectively. (C) 2015 Elsevier B.V. All rights reserved.
机译:纹理的感知特性的分析在诸如图像的语义描述,使用语言查询的基于内容的图像检索或基于低级视觉特征的专家系统设计等任务中起着基本作用。这些属性在图像中的存在由于它们的不精确性而非常难以表征,此外,因为它们的感知可能会根据用户或图像上下文而改变。在本文中,通过考虑人类感知的主观性的自适应模糊方法对纹理特性进行建模。为此,已经提出了两个阶段的方法。首先,获得非自适应模糊模型,该模型表示人类对纹理特性的存在的平均感知。对于此建模,我们建议学习特性的代表性度量与人类受试者给出的评估之间的关系。在第二阶段,对获得的模糊集进行调整,以便对用户可能具有的属性的特定感知以及受图像上下文影响的感知变化进行建模。为此,分别根据用户给出的信息或从图像上下文中提取的信息自动转换成员资格功能。 (C)2015 Elsevier B.V.保留所有权利。

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