Object Classification in Images of Neoclassical Furniture Using Deep Learning - Computational History and Data-Driven Humanities Access content directly
Conference Papers Year : 2016

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.
Fichier principal
Vignette du fichier
431566_1_En_10_Chapter.pdf (148.12 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01616309 , version 1 (13-10-2017)

Licence

Attribution

Identifiers

Cite

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⟩
119 View
82 Download

Altmetric

Share

Gmail Facebook X LinkedIn More