Object-Based Image Retrieval Using the U-Net-Based Neural Network

Day by day, all the research communities have been focusing on digital image retrieval due to more internet and social media uses. In this paper, a U-Net-based neural network is proposed for the segmentation process and Haar DWT and lifting wavelet schemes are used for feature extraction in content-...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Sandeep Kumar, Arpit Jain, Ambuj Kumar Agarwal, Shilpa Rani, Anshu Ghimire
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/ca265a49b19b4a8f92e15914c418a331
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:ca265a49b19b4a8f92e15914c418a331
record_format	dspace
spelling	oai:doaj.org-article:ca265a49b19b4a8f92e15914c418a3312021-11-22T01:10:34ZObject-Based Image Retrieval Using the U-Net-Based Neural Network1687-527310.1155/2021/4395646https://doaj.org/article/ca265a49b19b4a8f92e15914c418a3312021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/4395646https://doaj.org/toc/1687-5273Day by day, all the research communities have been focusing on digital image retrieval due to more internet and social media uses. In this paper, a U-Net-based neural network is proposed for the segmentation process and Haar DWT and lifting wavelet schemes are used for feature extraction in content-based image retrieval (CBIR). Haar wavelet is preferred as it is easy to understand, very simple to compute, and the fastest. The U-Net-based neural network (CNN) gives more accurate results than the existing methodology because deep learning techniques extract low-level and high-level features from the input image. For the evaluation process, two benchmark datasets are used, and the accuracy of the proposed method is 93.01% and 88.39% on Corel 1K and Corel 5K. U-Net is used for the segmentation purpose, and it reduces the dimension of the feature vector and feature extraction time by 5 seconds compared to the existing methods. According to the performance analysis, the proposed work has proven that U-Net improves image retrieval performance in terms of accuracy, precision, and recall on both the benchmark datasets.Sandeep KumarArpit JainAmbuj Kumar AgarwalShilpa RaniAnshu GhimireHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7Neurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENComputational Intelligence and Neuroscience, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Sandeep Kumar Arpit Jain Ambuj Kumar Agarwal Shilpa Rani Anshu Ghimire Object-Based Image Retrieval Using the U-Net-Based Neural Network
description	Day by day, all the research communities have been focusing on digital image retrieval due to more internet and social media uses. In this paper, a U-Net-based neural network is proposed for the segmentation process and Haar DWT and lifting wavelet schemes are used for feature extraction in content-based image retrieval (CBIR). Haar wavelet is preferred as it is easy to understand, very simple to compute, and the fastest. The U-Net-based neural network (CNN) gives more accurate results than the existing methodology because deep learning techniques extract low-level and high-level features from the input image. For the evaluation process, two benchmark datasets are used, and the accuracy of the proposed method is 93.01% and 88.39% on Corel 1K and Corel 5K. U-Net is used for the segmentation purpose, and it reduces the dimension of the feature vector and feature extraction time by 5 seconds compared to the existing methods. According to the performance analysis, the proposed work has proven that U-Net improves image retrieval performance in terms of accuracy, precision, and recall on both the benchmark datasets.
format	article
author	Sandeep Kumar Arpit Jain Ambuj Kumar Agarwal Shilpa Rani Anshu Ghimire
author_facet	Sandeep Kumar Arpit Jain Ambuj Kumar Agarwal Shilpa Rani Anshu Ghimire
author_sort	Sandeep Kumar
title	Object-Based Image Retrieval Using the U-Net-Based Neural Network
title_short	Object-Based Image Retrieval Using the U-Net-Based Neural Network
title_full	Object-Based Image Retrieval Using the U-Net-Based Neural Network
title_fullStr	Object-Based Image Retrieval Using the U-Net-Based Neural Network
title_full_unstemmed	Object-Based Image Retrieval Using the U-Net-Based Neural Network
title_sort	object-based image retrieval using the u-net-based neural network
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/ca265a49b19b4a8f92e15914c418a331
work_keys_str_mv	AT sandeepkumar objectbasedimageretrievalusingtheunetbasedneuralnetwork AT arpitjain objectbasedimageretrievalusingtheunetbasedneuralnetwork AT ambujkumaragarwal objectbasedimageretrievalusingtheunetbasedneuralnetwork AT shilparani objectbasedimageretrievalusingtheunetbasedneuralnetwork AT anshughimire objectbasedimageretrievalusingtheunetbasedneuralnetwork
_version_	1718418368723156992

Object-Based Image Retrieval Using the U-Net-Based Neural Network

Ejemplares similares