Fruit-Fly optimization based feature integration in image retrieval

The content-based image retrieval (CBIR) system searches and retrieves the similar images from the huge database using the significant features extracted from the image. Feature integration techniques used in the CBIR system assign static weights to each feature involved in the retrieval process tha...

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Autores principales:	Pavithra Latha Kumaresan, Subbulakshmi Pasupathi, Sindhia Lingaswamy, Sreesharmila Thangaswamy, Vimal Shunmuganathan, Danilo Pelusi
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	content based image retrieval dominant color descriptor radial difference pattern fruit fly optimization similarity measure Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/f85687d1cb5644dbaa8c72ab3f96a82f
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id	oai:doaj.org-article:f85687d1cb5644dbaa8c72ab3f96a82f
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spelling	oai:doaj.org-article:f85687d1cb5644dbaa8c72ab3f96a82f2021-11-11T01:11:03ZFruit-Fly optimization based feature integration in image retrieval10.3934/mbe.20213091551-0018https://doaj.org/article/f85687d1cb5644dbaa8c72ab3f96a82f2021-07-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021309?viewType=HTMLhttps://doaj.org/toc/1551-0018The content-based image retrieval (CBIR) system searches and retrieves the similar images from the huge database using the significant features extracted from the image. Feature integration techniques used in the CBIR system assign static weights to each feature involved in the retrieval process that gives a smaller number of similar images as a result. Moreover, the retrieval time of the CBIR system increases due to the entire database search. To overcome this disadvantage the proposed work introduced a two-level searching process in the CBIR system. The initial level of the proposed framework uses the image selection rule to select more relevant images for the second-level process. The second level of the framework takes the proposed dominant color and radial difference pattern details from the query and selected images. By using color and texture features of the selected images, similarity measure is calculated. The proposed work assigns optimal dynamic weight to the similarity measure of color and texture features using the fruit fly optimization algorithm. This improves the retrieval performance of the CBIR system.Pavithra Latha KumaresanSubbulakshmi Pasupathi Sindhia LingaswamySreesharmila ThangaswamyVimal ShunmuganathanDanilo Pelusi AIMS Pressarticlecontent based image retrievaldominant color descriptorradial difference patternfruit fly optimizationsimilarity measureBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 5, Pp 6178-6197 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	content based image retrieval dominant color descriptor radial difference pattern fruit fly optimization similarity measure Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	content based image retrieval dominant color descriptor radial difference pattern fruit fly optimization similarity measure Biotechnology TP248.13-248.65 Mathematics QA1-939 Pavithra Latha Kumaresan Subbulakshmi Pasupathi Sindhia Lingaswamy Sreesharmila Thangaswamy Vimal Shunmuganathan Danilo Pelusi Fruit-Fly optimization based feature integration in image retrieval
description	The content-based image retrieval (CBIR) system searches and retrieves the similar images from the huge database using the significant features extracted from the image. Feature integration techniques used in the CBIR system assign static weights to each feature involved in the retrieval process that gives a smaller number of similar images as a result. Moreover, the retrieval time of the CBIR system increases due to the entire database search. To overcome this disadvantage the proposed work introduced a two-level searching process in the CBIR system. The initial level of the proposed framework uses the image selection rule to select more relevant images for the second-level process. The second level of the framework takes the proposed dominant color and radial difference pattern details from the query and selected images. By using color and texture features of the selected images, similarity measure is calculated. The proposed work assigns optimal dynamic weight to the similarity measure of color and texture features using the fruit fly optimization algorithm. This improves the retrieval performance of the CBIR system.
format	article
author	Pavithra Latha Kumaresan Subbulakshmi Pasupathi Sindhia Lingaswamy Sreesharmila Thangaswamy Vimal Shunmuganathan Danilo Pelusi
author_facet	Pavithra Latha Kumaresan Subbulakshmi Pasupathi Sindhia Lingaswamy Sreesharmila Thangaswamy Vimal Shunmuganathan Danilo Pelusi
author_sort	Pavithra Latha Kumaresan
title	Fruit-Fly optimization based feature integration in image retrieval
title_short	Fruit-Fly optimization based feature integration in image retrieval
title_full	Fruit-Fly optimization based feature integration in image retrieval
title_fullStr	Fruit-Fly optimization based feature integration in image retrieval
title_full_unstemmed	Fruit-Fly optimization based feature integration in image retrieval
title_sort	fruit-fly optimization based feature integration in image retrieval
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/f85687d1cb5644dbaa8c72ab3f96a82f
work_keys_str_mv	AT pavithralathakumaresan fruitflyoptimizationbasedfeatureintegrationinimageretrieval AT subbulakshmipasupathi fruitflyoptimizationbasedfeatureintegrationinimageretrieval AT sindhialingaswamy fruitflyoptimizationbasedfeatureintegrationinimageretrieval AT sreesharmilathangaswamy fruitflyoptimizationbasedfeatureintegrationinimageretrieval AT vimalshunmuganathan fruitflyoptimizationbasedfeatureintegrationinimageretrieval AT danilopelusi fruitflyoptimizationbasedfeatureintegrationinimageretrieval
_version_	1718439645397647360

Fruit-Fly optimization based feature integration in image retrieval

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