Perception-based fuzzy partitions for visual texture modelling Martínez-Jiménez, Pedro Manuel Chamorro Martínez, Jesús Soto Hidalgo, José Manuel Image analysis Feature extraction Texture modeling Fuzzy partitions Linguistic labels Human perception Visual textures in images are usually described by humans using linguistic terms related to their perceptual properties, like “very coarse”, “low directional”, or “high contrasted”. Computational models with the ability of providing a perceptual texture characterization on the basis of these terms can be very useful in tasks like semantic description of images, content-based image retrieval using linguistic queries, or expert systems design based on low level visual features. In this paper, we address the problem of simulating the human perception of texture, obtaining linguistic labels to describe it in natural language. For this modeling, fuzzy partitions defined on the domain of some of the most representative measures of each property are employed. In order to define the fuzzy partitions, the number of linguistic labels and the parameters of the membership functions are calculated taking into account the relationship between the computational values given by the measures and the human perception of the corresponding property. The performance of each fuzzy partition is analyzed and tested using the human assessments, and a ranking of measures is obtained according to their ability to represent the perception of the property, allowing to identify the most suitable measure. 2019-05-22T11:47:10Z 2019-05-22T11:47:10Z 2018-04 info:eu-repo/semantics/article http://hdl.handle.net/10481/55785 10.1016/j.fss.2017.04.015 eng http://creativecommons.org/licenses/by-nc-nd/3.0/ info:eu-repo/semantics/embargoedAccess Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License