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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2012
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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. |
format | Online Article Text |
id | pubmed-3458107 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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