Enhanced Semantic Natural Scenery Retrieval System Through Novel Dominant Colour and Multi-Resolution Texture Feature Learning Model

IF 2.3 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Expert Systems Pub Date : 2024-12-04 DOI:10.1111/exsy.13805
L. K. Pavithra, P. Subbulakshmi, Nirmala Paramanandham, S. Vimal, Norah Saleh Alghamdi, Gaurav Dhiman
{"title":"Enhanced Semantic Natural Scenery Retrieval System Through Novel Dominant Colour and Multi-Resolution Texture Feature Learning Model","authors":"L. K. Pavithra,&nbsp;P. Subbulakshmi,&nbsp;Nirmala Paramanandham,&nbsp;S. Vimal,&nbsp;Norah Saleh Alghamdi,&nbsp;Gaurav Dhiman","doi":"10.1111/exsy.13805","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>A conventional content-based image retrieval system (CBIR) extracts image features from every pixel of the images, and its depiction of the feature is entirely different from human perception. Additionally, it takes a significant amount of time for retrieval. An optimal combination of appropriate image features is necessary to bridge the semantic gap between user queries and retrieval responses. Furthermore, users should require minimal interactions with the CBIR system to obtain accurate responses. Therefore, the proposed work focuses on extracting highly relevant feature information from a set of images in various natural image databases. Subsequently, a feature-based learning/classification model is introduced before similarity measure calculations, aiming to minimise retrieval time and the number of comparisons. The proposed work analyses the learning models based on the retrieval system's performance separately for the following features: (i) dominant colour, (ii) multi-resolution radial difference texture patterns, and a combination of both. The developed work is assessed with other techniques, and the results are reported. The results demonstrate that the implemented ensemble learning model-based CBIR outperforms the recent CBIR techniques.</p>\n </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 2","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Expert Systems","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/exsy.13805","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

A conventional content-based image retrieval system (CBIR) extracts image features from every pixel of the images, and its depiction of the feature is entirely different from human perception. Additionally, it takes a significant amount of time for retrieval. An optimal combination of appropriate image features is necessary to bridge the semantic gap between user queries and retrieval responses. Furthermore, users should require minimal interactions with the CBIR system to obtain accurate responses. Therefore, the proposed work focuses on extracting highly relevant feature information from a set of images in various natural image databases. Subsequently, a feature-based learning/classification model is introduced before similarity measure calculations, aiming to minimise retrieval time and the number of comparisons. The proposed work analyses the learning models based on the retrieval system's performance separately for the following features: (i) dominant colour, (ii) multi-resolution radial difference texture patterns, and a combination of both. The developed work is assessed with other techniques, and the results are reported. The results demonstrate that the implemented ensemble learning model-based CBIR outperforms the recent CBIR techniques.

基于主色和多分辨率纹理特征学习模型的语义自然风光检索系统
传统的基于内容的图像检索系统(CBIR)从图像的每个像素提取图像特征,其对特征的描述与人类感知完全不同。此外,它需要花费大量的时间进行检索。适当的图像特征的最佳组合是必要的,以弥合用户查询和检索响应之间的语义差距。此外,用户应该尽可能减少与CBIR系统的交互,以获得准确的响应。因此,本文的工作重点是从各种自然图像数据库中的一组图像中提取高度相关的特征信息。随后,在相似性度量计算之前引入基于特征的学习/分类模型,旨在最大限度地减少检索时间和比较次数。提出的工作分析了基于检索系统性能的学习模型,分别针对以下特征:(i)主色,(ii)多分辨率径向差纹理模式,以及两者的组合。已开发的工作用其他技术进行评估,并报告了结果。结果表明,所实现的基于集成学习模型的CBIR优于最近的CBIR技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Expert Systems
Expert Systems 工程技术-计算机:理论方法
CiteScore
7.40
自引率
6.10%
发文量
266
审稿时长
24 months
期刊介绍: Expert Systems: The Journal of Knowledge Engineering publishes papers dealing with all aspects of knowledge engineering, including individual methods and techniques in knowledge acquisition and representation, and their application in the construction of systems – including expert systems – based thereon. Detailed scientific evaluation is an essential part of any paper. As well as traditional application areas, such as Software and Requirements Engineering, Human-Computer Interaction, and Artificial Intelligence, we are aiming at the new and growing markets for these technologies, such as Business, Economy, Market Research, and Medical and Health Care. The shift towards this new focus will be marked by a series of special issues covering hot and emergent topics.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信