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Authors: Tiziano Portenier 1 ; Qiyang Hu 1 ; Paolo Favaro 1 and Matthias Zwicker 2

Affiliations: 1 University of Bern, Switzerland ; 2 University of Maryland, United States

ISBN: 978-989-758-290-5

Keyword(s): Image Retrieval, Deep Learning, Autoencoders.

Abstract: In this paper we develop a representation for fine-grained retrieval. Given a query, we want to retrieve data items of the same class, and, in addition, rank these items according to intra-class similarity. In our training data we assume partial knowledge: class labels are available, but the intra-class attributes are not. To compensate for this knowledge gap we propose using an autoencoder, which can be trained to produce features both with and without labels. Our main hypothesis is that network architectures that incorporate an autoencoder can learn features that meaningfully cluster data based on the intra-class variability. We propose and compare different architectures to construct our features, including a Siamese autoencoder (SAE), a classifying autoencoder (CAE) and a separate classifier-autoencoder (SCA). We find that these architectures indeed improve fine-grained retrieval compared to features trained purely in a supervised fashion for classification. We perform ex periments on four datasets, and observe that the SCA generally outperforms the other two. In particular, we obtain state of the art performance on fine-grained sketch retrieval. (More)

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Paper citation in several formats:
Portenier, T.; Hu, Q.; Favaro, P. and Zwicker, M. (2018). Fine-Grained Retrieval with Autoencoders.In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5 VISAPP: VISAPP, ISBN 978-989-758-290-5, pages 85-95. DOI: 10.5220/0006602100850095

@conference{visapp18,
author={Tiziano Portenier. and Qiyang Hu. and Paolo Favaro. and Matthias Zwicker.},
title={Fine-Grained Retrieval with Autoencoders},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5 VISAPP: VISAPP,},
year={2018},
pages={85-95},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006602100850095},
isbn={978-989-758-290-5},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5 VISAPP: VISAPP,
TI - Fine-Grained Retrieval with Autoencoders
SN - 978-989-758-290-5
AU - Portenier, T.
AU - Hu, Q.
AU - Favaro, P.
AU - Zwicker, M.
PY - 2018
SP - 85
EP - 95
DO - 10.5220/0006602100850095

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