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Texture Segmentation: An Objective Comparison between Traditional and Deep-Learning Methodologies

Karabağ, C. ORCID: 0000-0003-4424-0471, Verhoeven, J. ORCID: 0000-0002-0738-8517, Miller, N. & Reyes-Aldasoro, C. C. ORCID: 0000-0002-9466-2018 (2019). Texture Segmentation: An Objective Comparison between Traditional and Deep-Learning Methodologies. Preprints, doi: 10.20944/preprints201908.0001.v1


This paper compares a series of traditional and deep learning methodologies for the segmentation of textures. Six well-known texture composites first published by Randen and Hus{\o}y were used to compare traditional segmentation techniques (co-occurrence, filtering, local binary patterns, watershed, multiresolution sub-band filtering) against a deep-learning approach based on the U-Net architecture. For the latter, the effects of depth of the network, number of epochs and different optimisation algorithms were investigated. Overall, the best results were provided by the deep-learning approach. However, the best results were distributed within the parameters, and many configurations provided results well below the traditional techniques.

Publication Type: Article
Additional Information: This is the pre peer reviewed version of an article published as Texture Segmentation: An Objective Comparison between Five Traditional Algorithms and a Deep-Learning U-Net Architecture in Applied Science, available online at This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Publisher Keywords: texture; segmentation; deep learning
Subjects: P Language and Literature
Departments: School of Health & Psychological Sciences > Language & Communication Science
School of Science & Technology > Computer Science > giCentre
School of Science & Technology > Engineering
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