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Communication Dans Un Congrès Année : 2019

Challenging deep image descriptors for retrieval in heterogeneous iconographic collections

Résumé

This article proposes to study the behavior of recent and efficient state-of-the-art deep-learning based image descriptors for content-based image retrieval, facing a panel of complex variations appearing in heterogeneous image datasets, in particular in cultural collections that may involve multi-source, multi-date and multi-view Permission to make digital
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Dates et versions

hal-02273236 , version 1 (18-09-2019)

Identifiants

Citer

Dimitri Gominski, Martyna Poreba, Valérie Gouet-Brunet, Liming Chen. Challenging deep image descriptors for retrieval in heterogeneous iconographic collections. Proceedings of the 1st workshop on Structuring and Understanding of Multimedia heritAge Contents (SUMAC'19 Workshop @ ACM Multimedia 2019), Oct 2019, Nice, France. ⟨10.1145/3347317.3357246⟩. ⟨hal-02273236⟩
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