Image Analytics: A consolidation of visual feature extraction methods

Abstract

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.

Keywords:

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

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplemental Data

Supplemental data for this article can be accessed at https://doi.org/10.1080/23270012.2021.1998801

Notes

1 https://docs.opencv.org/master/d9/df8/tutorial_root.html

2 https://cloud.google.com/vision

3 https://azure.microsoft.com/en-au/try/cognitive-services/?api=face-api

4 https://www.ibm.com/watson/services/visual-recognition

5 https://aws.amazon.com/cn/rekognition

6 https://github.com/aimerykong/deepImageAestheticsAnalysis

7 http://memorability.csail.mit.edu/demo.html