Deep Watching: Towards New Methods of Analyzing Visual Media in Cultural Studies

Item Type Journal paper
Abstract

A large number of digital humanities projects focuses on text. This medial limitation may be attributed to the abundance of well-established quantitative methods applicable to text. Cultural Studies, however, analyse cultural expressions in a broad sense, including different non-textual media, physical artefacts, and performative actions. It is, to a certain extent, possible to transcribe these multi-medial phenomena in textual form; however, this transcription is difficult to automate and some information may be lost. Thus, quantitative approaches which directly access media-specific information are a desideratum for Cultural Studies.

Visual media constitute a significant part of cultural production. In our paper, we propose Deep Watching as a way to analyze visual media (films, photographs, and video clips) using cutting-edge machine learning and computer vision algorithms. Unlike previous approaches, which were based on generic information such as frame differences (Howanitz 2015), color distribution (Burghardt/Wolff 2016) or used manual annotation altogether (Dunst/Hartel 2016), Deep Watching allows to automatically identify visual information (symbols, objects, persons, body language, visual configuration of the scene) in large image and video corpora. To a certain extent, Tilton and Arnold’s Distant-Viewing Toolkit uses a comparable approach (Tilton/Arnold 2018). However, by means of our customized training of state-of-the-art convolutional neural networks for object detection and face recognition we can, in comparison to this toolkit, automatically extract more information about individual frames and their contexts.

Authors Bermeitinger, Bernhard; Gassner, Sebastian; Handschuh, Siegfried; Howanitz, Gernot; Radisch, Erik & Rehbein, Malte
Language English
Subjects computer science
social sciences
cultural studies
political science
HSG Classification contribution to scientific community
Refereed Yes
Date July 2019
Official URL https://dev.clariah.nl/files/dh2019/boa/0335.html
Contact Email Address bernhard.bermeitinger@unisg.ch
Depositing User Bernhard Bermeitinger
Date Deposited 14 Oct 2019 09:42
Last Modified 14 Oct 2019 09:42
URI: https://www.alexandria.unisg.ch/publications/258103

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Citation

Bermeitinger, Bernhard; Gassner, Sebastian; Handschuh, Siegfried; Howanitz, Gernot; Radisch, Erik & Rehbein, Malte (2019) Deep Watching: Towards New Methods of Analyzing Visual Media in Cultural Studies.

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https://www.alexandria.unisg.ch/id/eprint/258103
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