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Active Descriptor Learning for Feature Matching

  • Aziz Koçanaoğulları
  • Esra Ataer-CansızoğluEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11132)

Abstract

Feature descriptor extraction lies at the core of many computer vision tasks including image retrieval and registration. In this paper, we present an active learning method for extracting efficient features to be used in matching image patches. We train a Siamese deep neural network by optimizing a triplet loss function. We develop a more efficient and faster training procedure compared to the state-of-the-art methods by increasing difficulty during batch training. We achieve this by adjusting the margin in the loss and picking harder samples over time. The experiments are carried out on Photo Tourism dataset. The results show a significant improvement on matching performance and faster convergence in training.

Keywords

Feature matching Active learning Curriculum learning 

Notes

Acknowledgements

We thank Alan Sullivan, Radu Corcodel and Anoop Cherian for their helpful comments. This work was supported by and done at MERL.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Northeastern UniversityBostonUSA
  2. 2.Mitsubishi Electric Research Labs (MERL)CambridgeUSA

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