Article:
Computer Vision and the Digital Humanities: Adapting Image Processing Algorithms and Ground Truth through Active Learning

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dc.creatorMusik, Christoph
dc.creatorZeppelzauer, Matthias
dc.date.accessioned2020-08-24T13:55:00Z
dc.date.available2020-08-24T13:55:00Z
dc.date.issued2018-12-31
dc.description.abstractAutomated computer vision methods and tools offer new ways of analysing audio-visual material in the realm of the Digital Humanities (DH). While there are some promising results where these tools can be applied, there are basic challenges, such as algorithmic bias and the lack of sufficient transparency, one needs to carefully use these tools in a productive and responsible way. When it comes to the socio-technical understanding of computer vision tools and methods, a major unit of sociological analysis, attentiveness, and access for configuration (for both computer vision scientists and DH scholars) is what computer science calls “ground truth”. What is specified in the ground truth is the template or rule to follow, e.g. what an object looks like. This article aims at providing scholars in the DH with knowledge about how automated tools for image analysis work and how they are constructed. Based on these insights, the paper introduces an approach called “active learning” that can help to configure these tools in ways that fit the specific requirements and research questions of the DH in a more adaptive and user-centered way. We argue that both objectives need to be addressed, as this is, by all means, necessary for a successful implementation of computer vision tools in the DH and related fields.en
dc.identifier.doi10.18146/2213-0969.2018.jethc153
dc.identifier.doihttp://dx.doi.org/10.25969/mediarep/14754
dc.identifier.urihttps://mediarep.org/handle/doc/15734
dc.languageeng
dc.publisherNetherlands Institute for Sound and Vision
dc.publisher.placeHilversum
dc.relation.isPartOfissn:2213-0969
dc.relation.ispartofseriesVIEW Journal of European Television History and Culture
dc.rightsCreative Commons Attribution Share Alike 4.0 Generic
dc.rights.urihttps://creativecommons.org/licenses/by-sa/4.0
dc.subjectFernsehende
dc.subjectExplainable Artificial Intelligenceen
dc.subjectDigital Humanitiesen
dc.subjectGround Truth Generationen
dc.subjectMachine Learningen
dc.subjectActive Learningen
dc.subjectImage Understandingen
dc.subjectComputer Visionen
dc.subject.ddcddc:070
dc.subject.ddcddc:791
dc.titleComputer Vision and the Digital Humanities: Adapting Image Processing Algorithms and Ground Truth through Active Learningen
dc.typearticle
dc.type.statuspublishedVersion
dspace.entity.typeArticleen
local.coverpage2021-05-29T06:13:15
local.identifier.firstpublishedhttps://doi.org/10.18146/2213-0969.2018.jethc153
local.source.epage72
local.source.issue14
local.source.spage59
local.source.volume7

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2018 | 14 | Audiovisual Data in Digital Humanities