Invariant descriptor learning using a Siamese convolutional neural network

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Chen, L.; Rottensteiner, F.; Heipke, C.: Invariant descriptor learning using a Siamese convolutional neural network. In: XXIII ISPRS Congress, Commission III 3 (2016), Nr. 3, S. 11-18. DOI: https://doi.org/10.5194/isprsannals-III-3-11-2016

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To cite the version in the repository, please use this identifier: https://doi.org/10.15488/1176

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Sum total of downloads: 279




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Abstract: 
In this paper we describe learning of a descriptor based on the Siamese Convolutional Neural Network (CNN) architecture and evaluate our results on a standard patch comparison dataset. The descriptor learning architecture is composed of an input module, a Siamese CNN descriptor module and a cost computation module that is based on the L2 Norm. The cost function we use pulls the descriptors of matching patches close to each other in feature space while pushing the descriptors for non-matching pairs away from each other. Compared to related work, we optimize the training parameters by combining a moving average strategy for gradients and Nesterov's Accelerated Gradient. Experiments show that our learned descriptor reaches a good performance and achieves state-of-art results in terms of the false positive rate at a 95% recall rate on standard benchmark datasets.
License of this version: CC BY 3.0 Unported
Document Type: article
Publishing status: publishedVersion
Issue Date: 2016
Appears in Collections:Fakultät für Bauingenieurwesen und Geodäsie

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pos. country downloads
total perc.
1 image of flag of Germany Germany 128 45.88%
2 image of flag of United States United States 26 9.32%
3 image of flag of China China 26 9.32%
4 image of flag of India India 15 5.38%
5 image of flag of No geo information available No geo information available 11 3.94%
6 image of flag of France France 10 3.58%
7 image of flag of Hong Kong Hong Kong 8 2.87%
8 image of flag of Korea, Republic of Korea, Republic of 7 2.51%
9 image of flag of Switzerland Switzerland 6 2.15%
10 image of flag of United Kingdom United Kingdom 5 1.79%
    other countries 37 13.26%

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