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Authors: R. Del Chiaro ; A Bagdanov and A. Del Bimbo

Affiliation: Media Integration and Communication Center, University of Florence and Italy

Keyword(s): Cultural Heritage, Computer Vision, Instance Recognition, Image Categorization, Webly-supervised Learning.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Computer Vision, Visualization and Computer Graphics ; Imaging for Cultural Heritage (Modeling/Simulation, Virtual Restoration)

Abstract: This paper describes the NoisyArt dataset, a dataset designed to support research on webly-supervised recognition of artworks. The dataset consists of more than 90,000 images and in more than 3,000 webly-supervised classes, and a subset of 200 classes with verified test images. Candidate artworks are identified using publicly available metadata repositories, and images are automatically acquired using Google Image and Flickr search. Document embeddings are also provided for short descriptions of all artworks. NoisyArt is designed to support research on webly-supervised artwork instance recognition, zero-shot learning, and other approaches to visual recognition of cultural heritage objects. Baseline experimental results are given using pretrained Convolutional Neural Network (CNN) features and a shallow classifier architecture. Experiments are also performed using a variety of techniques for identifying and mitigating label noise in webly-supervised training data.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Del Chiaro, R.; Bagdanov, A. and Del Bimbo, A. (2019). NoisyArt: A Dataset for Webly-supervised Artwork Recognition. In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 4: VISAPP; ISBN 978-989-758-354-4; ISSN 2184-4321, SciTePress, pages 467-475. DOI: 10.5220/0007392704670475

@conference{visapp19,
author={R. {Del Chiaro}. and A Bagdanov. and A. {Del Bimbo}.},
title={NoisyArt: A Dataset for Webly-supervised Artwork Recognition},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 4: VISAPP},
year={2019},
pages={467-475},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007392704670475},
isbn={978-989-758-354-4},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 4: VISAPP
TI - NoisyArt: A Dataset for Webly-supervised Artwork Recognition
SN - 978-989-758-354-4
IS - 2184-4321
AU - Del Chiaro, R.
AU - Bagdanov, A.
AU - Del Bimbo, A.
PY - 2019
SP - 467
EP - 475
DO - 10.5220/0007392704670475
PB - SciTePress