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 | 20 Jul 2022 17:39 |
URI: | https://www.alexandria.unisg.ch/publications/258103 |
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CitationBermeitinger, Bernhard; Gassner, Sebastian; Handschuh, Siegfried; Howanitz, Gernot; Radisch, Erik & Rehbein, Malte (2019) Deep Watching: Towards New Methods of Analyzing Visual Media in Cultural Studies. Statisticshttps://www.alexandria.unisg.ch/id/eprint/258103
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