Siamese network features for image matching |
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Author: | Melekhov, Iaroslav1; Kannala, Juho1; Rahtu, Esa2 |
Organizations: |
1Department of Computer Science, Aalto University, Finland 2Center for Machine Vision Research, University of Oulu |
Format: | article |
Version: | accepted version |
Access: | open |
Online Access: | PDF Full Text (PDF, 1.9 MB) |
Persistent link: | http://urn.fi/urn:nbn:fi-fe2019090526960 |
Language: | English |
Published: |
Institute of Electrical and Electronics Engineers,
2016
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Publish Date: | 2019-09-05 |
Description: |
AbstractFinding matching images across large datasets plays a key role in many computer vision applications such as structure-from-motion (SfM), multi-view 3D reconstruction, image retrieval, and image-based localisation. In this paper, we propose finding matching and non-matching pairs of images by representing them with neural network based feature vectors, whose similarity is measured by Euclidean distance. The feature vectors are obtained with convolutional neural networks which are learnt from labeled examples of matching and non-matching image pairs by using a contrastive loss function in a Siamese network architecture. Previously Siamese architecture has been utilised in facial image verification and in matching local image patches, but not yet in generic image retrieval or whole-image matching. Our experimental results show that the proposed features improve matching performance compared to baseline features obtained with networks which are trained for image classification task. The features generalize well and improve matching of images of new landmarks which are not seen at training time. This is despite the fact that the labeling of matching and non-matching pairs is imperfect in our training data. The results are promising considering image retrieval applications, and there is potential for further improvement by utilising more training image pairs with more accurate ground truth labels. see all
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ISBN: | 978-1-5090-4847-2 |
ISBN Print: | 978-1-5090-4848-9 |
Pages: | 378 - 383 |
DOI: | 10.1109/ICPR.2016.7899663 |
OADOI: | https://oadoi.org/10.1109/ICPR.2016.7899663 |
Host publication: |
2016 Proceedings of 23rd Internaltional conference on Pattern Recognition (ICPR 2016) |
Conference: |
International Conference on Pattern Recognition |
Type of Publication: |
A4 Article in conference proceedings |
Field of Science: |
113 Computer and information sciences 213 Electronic, automation and communications engineering, electronics |
Subjects: | |
Copyright information: |
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