HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

Object Classification in Images of Neoclassical Furniture Using Deep Learning

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.
Document type :
Conference papers
Complete list of metadata

Cited literature [7 references]  Display  Hide  Download

Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Friday, October 13, 2017 - 2:53:05 PM
Last modification on : Thursday, March 5, 2020 - 5:40:54 PM
Long-term archiving on: : Sunday, January 14, 2018 - 2:20:44 PM


Files produced by the author(s)


Distributed under a Creative Commons Attribution 4.0 International License



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. pp.109-112, ⟨10.1007/978-3-319-46224-0_10⟩. ⟨hal-01616309⟩



Record views


Files downloads