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Cross-year multi-modal image retrieval using siamese networks

Abstract : This paper introduces a multi-modal network that learns to retrieve by content vertical aerial images of French urban and rural territories taken about 15 years apart. This means it should be invariant against a big range of changes as the (nat-ural) landscape evolves over time. It leverages the original images and semantically segmented and labeled regions. The core of the method is a Siamese network that learns to extract features from corresponding image pairs across time. These descriptors are discriminative enough, such that a simple kNN classifier on top, suffices as final geo-matching criteria. The method outperformed SOTA "off-the-shelf" image descrip-tors GEM and ResNet50 on the new aerial images dataset.
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https://hal.archives-ouvertes.fr/hal-02903434
Contributor : Margarita Khokhlova Connect in order to contact the contributor
Submitted on : Tuesday, July 21, 2020 - 10:01:50 AM
Last modification on : Friday, September 30, 2022 - 11:34:16 AM
Long-term archiving on: : Tuesday, December 1, 2020 - 2:27:12 AM

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Margarita Khokhlova, Valérie Gouet-Brunet, Nathalie Abadie, Liming Chen. Cross-year multi-modal image retrieval using siamese networks. ICIP 2020 – 27th IEEE International Conference on Image Processing, Oct 2020, Abou Dhabi, United Arab Emirates. ⟨10.1109/ICIP40778.2020.9190662⟩. ⟨hal-02903434⟩

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