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.
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.
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.
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.
Abstract
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.
Keywords
texture; segmentation; deep learning
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
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.
