Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 30 July 2019 / Approved: 1 August 2019 / Online: 1 August 2019 (03:30:51 CEST)

How to cite: Karabag, C.; Verhoeven, J.; Miller, N.R.; Reyes-Aldasoro, C.C. Texture Segmentation: An Objective Comparison between Traditional and Deep-Learning Methodologies. Preprints 2019, 2019080001 (doi: 10.20944/preprints201908.0001.v1). Karabag, C.; Verhoeven, J.; Miller, N.R.; Reyes-Aldasoro, C.C. Texture Segmentation: An Objective Comparison between Traditional and Deep-Learning Methodologies. Preprints 2019, 2019080001 (doi: 10.20944/preprints201908.0001.v1).

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.

Subject Areas

texture; segmentation; deep learning

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