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
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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.

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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.
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