Image Analytics: A consolidation of visual feature extraction methods

Xiaohui Liu, Fei Liu, Yijing Li, Huizhang Shen, Eric T.K. Lim, Chee Wee Tan

Research output: Journal article publicationJournal articleAcademic researchpeer-review


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.

Original languageEnglish
Pages (from-to)569-597
Number of pages29
JournalJournal of Management Analytics
Issue number4
Publication statusPublished - Nov 2021


  • attribute extraction
  • computer vision
  • deep learning
  • Image analytics
  • Python

ASJC Scopus subject areas

  • Statistics and Probability
  • Business, Management and Accounting (miscellaneous)
  • Statistics, Probability and Uncertainty


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