图像分析:视觉特征提取方法的整合

IF 3.6 2区 管理学 Q2 BUSINESS
Xiaohui Liu, Fei Liu, Yijing Li, Huizhang Shen, Eric T.K. Lim, Chee-Wee Tan
{"title":"图像分析:视觉特征提取方法的整合","authors":"Xiaohui Liu, Fei Liu, Yijing Li, Huizhang Shen, Eric T.K. Lim, Chee-Wee Tan","doi":"10.1080/23270012.2021.1998801","DOIUrl":null,"url":null,"abstract":"<p>Revolutionary advances in machine and deep learning techniques within the field of computer field have dramatically expanded our opportunities to decipher the merits of digital imagery in the business world. Although extant literature on computer vision has yielded a myriad of approaches for extracting core attributes from images, the esotericism of the advocated techniques hinders scholars from delving into the role of visual rhetoric in driving business performance. Consequently, this tutorial aims to consolidate resources for extracting visual features via conventional machine and/or deep learning techniques. We describe resources and techniques based on three visual feature extraction methods, namely calculation-, recognition-, and simulation-based. Additionally, we offer practical examples to illustrate how image features can be accessed via open-sourced python packages such as OpenCV and TensorFlow.</p>","PeriodicalId":46290,"journal":{"name":"Journal of Management Analytics","volume":null,"pages":null},"PeriodicalIF":3.6000,"publicationDate":"2021-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Image Analytics: A consolidation of visual feature extraction methods\",\"authors\":\"Xiaohui Liu, Fei Liu, Yijing Li, Huizhang Shen, Eric T.K. Lim, Chee-Wee Tan\",\"doi\":\"10.1080/23270012.2021.1998801\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Revolutionary advances in machine and deep learning techniques within the field of computer field have dramatically expanded our opportunities to decipher the merits of digital imagery in the business world. Although extant literature on computer vision has yielded a myriad of approaches for extracting core attributes from images, the esotericism of the advocated techniques hinders scholars from delving into the role of visual rhetoric in driving business performance. Consequently, this tutorial aims to consolidate resources for extracting visual features via conventional machine and/or deep learning techniques. We describe resources and techniques based on three visual feature extraction methods, namely calculation-, recognition-, and simulation-based. Additionally, we offer practical examples to illustrate how image features can be accessed via open-sourced python packages such as OpenCV and TensorFlow.</p>\",\"PeriodicalId\":46290,\"journal\":{\"name\":\"Journal of Management Analytics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2021-11-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Management Analytics\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://doi.org/10.1080/23270012.2021.1998801\",\"RegionNum\":2,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BUSINESS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Management Analytics","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1080/23270012.2021.1998801","RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BUSINESS","Score":null,"Total":0}
引用次数: 0

摘要

计算机领域机器和深度学习技术的革命性进步极大地扩展了我们在商业世界中破译数字图像优点的机会。尽管现有的计算机视觉文献已经产生了无数从图像中提取核心属性的方法,但所提倡的技术的深奥性阻碍了学者们深入研究视觉修辞在推动业务绩效方面的作用。因此,本教程旨在整合通过传统机器和/或深度学习技术提取视觉特征的资源。我们描述了基于三种视觉特征提取方法的资源和技术,即基于计算、基于识别和基于模拟。此外,我们提供了一些实际的例子来说明如何通过开源的python包(如OpenCV和TensorFlow)访问图像特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Image Analytics: A consolidation of visual feature extraction methods

Revolutionary advances in machine and deep learning techniques within the field of computer field have dramatically expanded our opportunities to decipher the merits of digital imagery in the business world. Although extant literature on computer vision has yielded a myriad of approaches for extracting core attributes from images, the esotericism of the advocated techniques hinders scholars from delving into the role of visual rhetoric in driving business performance. Consequently, this tutorial aims to consolidate resources for extracting visual features via conventional machine and/or deep learning techniques. We describe resources and techniques based on three visual feature extraction methods, namely calculation-, recognition-, and simulation-based. Additionally, we offer practical examples to illustrate how image features can be accessed via open-sourced python packages such as OpenCV and TensorFlow.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of Management Analytics
Journal of Management Analytics SOCIAL SCIENCES, MATHEMATICAL METHODS-
CiteScore
13.30
自引率
3.40%
发文量
14
期刊介绍: The Journal of Management Analytics (JMA) is dedicated to advancing the theory and application of data analytics in traditional business fields. It focuses on the intersection of data analytics with key disciplines such as accounting, finance, management, marketing, production/operations management, and supply chain management. JMA is particularly interested in research that explores the interface between data analytics and these business areas. The journal welcomes studies employing a range of research methods, including empirical research, big data analytics, data science, operations research, management science, decision science, and simulation modeling.
×
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:481959085
Book学术官方微信