Object Classification in Images of Neoclassical Furniture Using Deep Learning

Item Type	Journal paper
Abstract	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.
Authors	Bermeitinger, Bernhard; Freitas, André; Donig, Simon & Handschuh, Siegfried
Journal or Publication Title	Computational History and Data-Driven Humanities
Language	English
Subjects	computer science
HSG Classification	contribution to scientific community
HSG Profile Area	None
Refereed	Yes
Date	5 May 2016
Publisher	Springer
Place of Publication	Cham
Page Range	109-112
Publisher DOI	https://doi.org/10.1007/978-3-319-46224-0_10
Official URL	https://link.springer.com/chapter/10.1007/978-3-31...
Contact Email Address	bernhard.bermeitinger@unisg.ch
Depositing User	Bernhard Bermeitinger
Date Deposited	02 Oct 2019 10:06
Last Modified	20 Jul 2022 17:39
URI:	https://www.alexandria.unisg.ch/publications/257997

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Bermeitinger, Bernhard; Freitas, André; Donig, Simon & Handschuh, Siegfried (2016) Object Classification in Images of Neoclassical Furniture Using Deep Learning. Computational History and Data-Driven Humanities, 109-112.

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https://www.alexandria.unisg.ch/id/eprint/257997

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