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.
2nd International Workshop on Computational History and Data-Driven Humanities (CHDDH), May 2016, Dublin, Ireland. IFIP Advances in Information and Communication Technology, AICT-482, pp.109-112, 2016, Computational History and Data-Driven Humanities. 〈10.1007/978-3-319-46224-0_10〉
https://hal.inria.fr/hal-01616309
Contributeur : Hal Ifip
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Bernhard Bermeitinger, André Freitas, Simon Donig, Siegfried Handschuh. Object Classification in Images of Neoclassical Furniture Using Deep Learning. 2nd International Workshop on Computational History and Data-Driven Humanities (CHDDH), May 2016, Dublin, Ireland. IFIP Advances in Information and Communication Technology, AICT-482, pp.109-112, 2016, Computational History and Data-Driven Humanities. 〈10.1007/978-3-319-46224-0_10〉. 〈hal-01616309〉
