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Texture classification for visual data using transfer learning

The texture is the most fundamental aspect of a picture that contributes to its recognition. Computer vision challenges such as picture identification and segmentation are built on the foundation of texture analysis. Various images of satellite, forestry, medical etc. have been identifiable because...

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Detalles Bibliográficos
Autores principales: Goyal, Vinat, Sharma, Sanjeev
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9739347/
https://www.ncbi.nlm.nih.gov/pubmed/36532597
http://dx.doi.org/10.1007/s11042-022-14276-y
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author Goyal, Vinat
Sharma, Sanjeev
author_facet Goyal, Vinat
Sharma, Sanjeev
author_sort Goyal, Vinat
collection PubMed
description The texture is the most fundamental aspect of a picture that contributes to its recognition. Computer vision challenges such as picture identification and segmentation are built on the foundation of texture analysis. Various images of satellite, forestry, medical etc. have been identifiable because of textures. This work aims to offer texture classification models that will outperform previously presented methods. In this work, transfer learning was applied to attain this goal. MobileNetV3 and InceptionV3 are the two pre-trained models employed. Brodatz, Kylberg, and Outex texture datasets were used to evaluate the models. The models achieved excellent results and achieved the objective in most cases. Classification accuracy obtained for the Kylberg dataset were 100% and 99.89%. For the Brodatz dataset, the classification accuracy obtained was 99.83% and 99.94%. For the Outex datasets, the classification accuracy obtained was 99.48% and 99.48%. The model outputs the corresponding label of the texture of the image.
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spelling pubmed-97393472022-12-12 Texture classification for visual data using transfer learning Goyal, Vinat Sharma, Sanjeev Multimed Tools Appl Article The texture is the most fundamental aspect of a picture that contributes to its recognition. Computer vision challenges such as picture identification and segmentation are built on the foundation of texture analysis. Various images of satellite, forestry, medical etc. have been identifiable because of textures. This work aims to offer texture classification models that will outperform previously presented methods. In this work, transfer learning was applied to attain this goal. MobileNetV3 and InceptionV3 are the two pre-trained models employed. Brodatz, Kylberg, and Outex texture datasets were used to evaluate the models. The models achieved excellent results and achieved the objective in most cases. Classification accuracy obtained for the Kylberg dataset were 100% and 99.89%. For the Brodatz dataset, the classification accuracy obtained was 99.83% and 99.94%. For the Outex datasets, the classification accuracy obtained was 99.48% and 99.48%. The model outputs the corresponding label of the texture of the image. Springer US 2022-12-10 /pmc/articles/PMC9739347/ /pubmed/36532597 http://dx.doi.org/10.1007/s11042-022-14276-y Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Goyal, Vinat
Sharma, Sanjeev
Texture classification for visual data using transfer learning
title Texture classification for visual data using transfer learning
title_full Texture classification for visual data using transfer learning
title_fullStr Texture classification for visual data using transfer learning
title_full_unstemmed Texture classification for visual data using transfer learning
title_short Texture classification for visual data using transfer learning
title_sort texture classification for visual data using transfer learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9739347/
https://www.ncbi.nlm.nih.gov/pubmed/36532597
http://dx.doi.org/10.1007/s11042-022-14276-y
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