Image Analytics: A Consolidation of Visual Feature Extraction Methods

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

Research output: Chapter in Book/Report/Conference proceedingConference abstract in proceedingsResearchpeer-review


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.
Original languageEnglish
Title of host publicationProceedings of the Eightieth Annual Meeting of the Academy of Management
EditorsGuclu Atinc
Number of pages1
Place of PublicationBriarcliff Manor, NY
PublisherAcademy of Management
Publication date2020
Publication statusPublished - 2020
EventThe Academy of Management Annual Meeting 2020: Broadening Our Sight - Virtual
Duration: 7 Aug 202011 Aug 2020
Conference number: 80


ConferenceThe Academy of Management Annual Meeting 2020
Internet address
SeriesAcademy of Management Proceedings

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