City Research Online

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

Karabağ, C., Verhoeven, J. ORCID: 0000-0002-0738-8517, Miller, N. and Reyes-Aldasoro, C. C. ORCID: 0000-0002-9466-2018 (2019). Texture Segmentation: An Objective Comparison between Five Traditional Algorithms and a Deep-Learning U-Net Architecture. Applied Sciences, 9, 3900.. doi: 10.3390/app9183900

Abstract

<jats:p>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.</jats:p>

Publication Type: Article
Additional Information: © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open accessarticle distributed under the terms and conditions of the Creative Commons Attribution(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Publisher Keywords: texture; segmentation; deep learning
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Departments: School of Health Sciences > Language & Communication Science
School of Mathematics, Computer Science & Engineering > Engineering > Electrical & Electronic Engineering
URI: https://openaccess.city.ac.uk/id/eprint/22853
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