Automated Image Analysis (AIA)

8 Pages Posted: 6 Sep 2022 Last revised: 12 Oct 2022

See all articles by Jochen Hartmann

Jochen Hartmann

TUM School of Management,Technical University of Munich

Samuel Domdey

Technical University Hamburg-Harburg (TUHH)

Date Written: October 11, 2022

Abstract

Images are rapidly increasing in importance for business and social science research. This is evidenced by an increasing number of publications leveraging visual information. However, the scale of these data often exceed human annotation capabilities and extracting features from images automatically is a complex task. While open-source code for automated image analysis exists, implementations are often scattered across different repositories, making their use burdensome for applied researchers. To address this issue, we provide a bundling of automated image analysis tools that enables researchers to easily extract 47 visual features from images, including low-level features such as image quality and brightness as well as high-level features such as face presence and prominence. All code that we consolidate is open-source and applying our pipeline requires no programming skills. Consequently, we hope our tool contributes to making images more accessible to address meaningful, substantive research problems.

Keywords: image analysis, deep learning, computer vision, image feature extraction

Suggested Citation

Hartmann, Jochen and Domdey, Samuel, Automated Image Analysis (AIA) (October 11, 2022). Available at SSRN: https://ssrn.com/abstract=4189586 or http://dx.doi.org/10.2139/ssrn.4189586

Jochen Hartmann (Contact Author)

TUM School of Management,Technical University of Munich ( email )

Arcisstrasse 21
Munchen, 80333
Germany

Samuel Domdey

Technical University Hamburg-Harburg (TUHH) ( email )

Schwarzenbergstrasse 95
Hamburg, DE Hamburg D-21071
Germany

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