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 metadatas

Cited literature [7 references]  Display  Hide  Download

https://hal.inria.fr/hal-01616309
Contributor : Hal Ifip <>
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

File

431566_1_En_10_Chapter.pdf
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

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⟩

Share

Metrics

Record views

296

Files downloads

189