Object Classification in Images of Neoclassical Furniture Using Deep Learning
Journal
Computational History and Data-Driven Humanities
Type
conference paper
Date Issued
2016-05-05
Author(s)
Abstract (De)
This short paper outlines research results on object classification in images of Neoclassical furniture. The motivation was to provide an object recognition framework which is able to support the alignment of furniture images with a symbolic level model. A data-driven bottom-up research routine in the Neoclassica research framework is the main use-case. This research framework is described more extensively by Donig et al. [2]. It strives to deliver tools for analyzing the spread of aesthetic forms which are considered as a cultural transfer process.
Language
English
HSG Classification
contribution to scientific community
HSG Profile Area
None
Refereed
Yes
Publisher
Springer
Publisher place
Cham
Start page
109
End page
112
Subject(s)
Division(s)
Contact Email Address
bernhard.bermeitinger@unisg.ch
Eprints ID
257997
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Bermeitinger et al_2016_Object Classification in Images of Neoclassical Furniture Using Deep Learning.pdf
Size
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Format
Adobe PDF
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