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Open AccessArticle

Texture Segmentation: An Objective Comparison between Five Traditional Algorithms and a Deep-Learning U-Net Architecture

1
Department of Electrical and Electronic Engineering, Research Centre for Biomedical Engineering, School of Mathematics, Computer Science and Engineering, City, University of London, London EC1V 0HB, UK
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School of Health Sciences, Division of Language & Communication Science, Phonetics Laboratory, University of London, London EC1R 1UW, UK
3
Department of Linguistics CLIPS, University of Antwerp, 2000 Antwerp, Belgium
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(18), 3900; https://doi.org/10.3390/app9183900
Received: 30 July 2019 / Revised: 22 August 2019 / Accepted: 10 September 2019 / Published: 17 September 2019
(This article belongs to the Special Issue Texture and Colour in Image Analysis)
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 Husøy 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. View Full-Text
Keywords: texture; segmentation; deep learning texture; segmentation; deep learning
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Karabağ, C.; Verhoeven, J.; Miller, N.R.; Reyes-Aldasoro, C.C. Texture Segmentation: An Objective Comparison between Five Traditional Algorithms and a Deep-Learning U-Net Architecture. Appl. Sci. 2019, 9, 3900.

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