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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 1/2022

27.04.2021 | Original Article

Triplet-object loss for large scale deep image retrieval

verfasst von: Jie Zhu, Yang Shu, Junsan Zhang, Xuanye Wang, Shufang Wu

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 1/2022

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Abstract

Deep hashing has been widely applied in large scale image retrieval due to its high computation efficiency and retrieval performance. Recently, training deep hashing networks with a triplet ranking loss become a common framework. However, most of the triplet ranking loss based deep hashing methods cannot obtain satisfactory retrieval performance due to their ignoring the relative similarities among the objects. In this paper, we propose a method to learn the discriminative object features and utilize these features to compute the adaptive margins of the proposed loss for learning powerful hash codes. Experimental results show that our learned hash codes can yield state-of-the-art retrieval performance on three challenging datasets
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Metadaten
Titel
Triplet-object loss for large scale deep image retrieval
verfasst von
Jie Zhu
Yang Shu
Junsan Zhang
Xuanye Wang
Shufang Wu
Publikationsdatum
27.04.2021
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 1/2022
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-021-01330-8

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