Abstract
Revolutionary advances in computer vision and deep learning have dramatically expanded our opportunities to decipher the merits of images in the business world. Though prior research on computer vision has yielded a myriad of approaches to extract core attributes from images, the esotericism of the advocated techniques hinders scholars from delving into the role of visuals in driving business performance. Consequently, this tutorial aims to consolidate resources of visual features’ extraction tactics that employ both conventional machine learning and deep learning models. We describe resources and techniques based on three visual feature extraction methods, namely calculation-, recognition-, and simulation-based method. Additionally, practical examples are provided to illustrate how image features can be accessed via python and open sourced packages such as OpenCV and TensorFlow.
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