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Research on Similarity Measurement for Texture Image Retrieval

A complete texture image retrieval system includes two techniques: texture feature extraction and similarity measurement. Specifically, similarity measurement is a key problem for texture image retrieval study. In this paper, we present an effective similarity measurement formula. The MIT vision tex...

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Detalles Bibliográficos
Autores principales: Zhu, Zhengli, Zhao, Chunxia, Hou, Yingkun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3458107/
https://www.ncbi.nlm.nih.gov/pubmed/23049785
http://dx.doi.org/10.1371/journal.pone.0045302
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author Zhu, Zhengli
Zhao, Chunxia
Hou, Yingkun
author_facet Zhu, Zhengli
Zhao, Chunxia
Hou, Yingkun
author_sort Zhu, Zhengli
collection PubMed
description A complete texture image retrieval system includes two techniques: texture feature extraction and similarity measurement. Specifically, similarity measurement is a key problem for texture image retrieval study. In this paper, we present an effective similarity measurement formula. The MIT vision texture database, the Brodatz texture database, and the Outex texture database were used to verify the retrieval performance of the proposed similarity measurement method. Dual-tree complex wavelet transform and nonsubsampled contourlet transform were used to extract texture features. Experimental results show that the proposed similarity measurement method achieves better retrieval performance than some existing similarity measurement methods.
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spelling pubmed-34581072012-10-03 Research on Similarity Measurement for Texture Image Retrieval Zhu, Zhengli Zhao, Chunxia Hou, Yingkun PLoS One Research Article A complete texture image retrieval system includes two techniques: texture feature extraction and similarity measurement. Specifically, similarity measurement is a key problem for texture image retrieval study. In this paper, we present an effective similarity measurement formula. The MIT vision texture database, the Brodatz texture database, and the Outex texture database were used to verify the retrieval performance of the proposed similarity measurement method. Dual-tree complex wavelet transform and nonsubsampled contourlet transform were used to extract texture features. Experimental results show that the proposed similarity measurement method achieves better retrieval performance than some existing similarity measurement methods. Public Library of Science 2012-09-25 /pmc/articles/PMC3458107/ /pubmed/23049785 http://dx.doi.org/10.1371/journal.pone.0045302 Text en © 2012 Zhu et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Zhu, Zhengli
Zhao, Chunxia
Hou, Yingkun
Research on Similarity Measurement for Texture Image Retrieval
title Research on Similarity Measurement for Texture Image Retrieval
title_full Research on Similarity Measurement for Texture Image Retrieval
title_fullStr Research on Similarity Measurement for Texture Image Retrieval
title_full_unstemmed Research on Similarity Measurement for Texture Image Retrieval
title_short Research on Similarity Measurement for Texture Image Retrieval
title_sort research on similarity measurement for texture image retrieval
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3458107/
https://www.ncbi.nlm.nih.gov/pubmed/23049785
http://dx.doi.org/10.1371/journal.pone.0045302
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