City Research Online

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

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 Five Traditional Algorithms and a Deep-Learning U-Net Architecture. Applied Sciences, 9(18), article number 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 & Psychological Sciences > Language & Communication Science
School of Science & Technology > Computer Science > giCentre
School of Science & Technology > Engineering
SWORD Depositor:
[thumbnail of applsci-09-03900.pdf]
Preview
Text - Published Version
Available under License Creative Commons Attribution.

Download (11MB) | Preview

Export

Add to AnyAdd to TwitterAdd to FacebookAdd to LinkedinAdd to PinterestAdd to Email

Downloads

Downloads per month over past year

View more statistics

Actions (login required)

Admin Login Admin Login