Fracking Deep Convolutional Image Descriptors
Abstract
In this paper we propose a novel framework for learning local image descriptors in a discriminative manner. For this purpose we explore a siamese architecture of Deep Convolutional Neural Networks (CNN), with a Hinge embedding loss on the L2 distance between descriptors. Since a siamese architecture uses pairs rather than single image patches to train, there exist a large number of positive samples and an exponential number of negative samples. We propose to explore this space with a stochastic sampling of the training set, in combination with an aggressive mining strategy over both the positive and negative samples which we denote as "fracking". We perform a thorough evaluation of the architecture hyper-parameters, and demonstrate large performance gains compared to both standard CNN learning strategies, hand-crafted image descriptors like SIFT, and the state-of-the-art on learned descriptors: up to 2.5x vs SIFT and 1.5x vs the state-of-the-art in terms of the area under the curve (AUC) of the Precision-Recall curve.
- Publication:
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arXiv e-prints
- Pub Date:
- December 2014
- DOI:
- 10.48550/arXiv.1412.6537
- arXiv:
- arXiv:1412.6537
- Bibcode:
- 2014arXiv1412.6537S
- Keywords:
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- Computer Science - Computer Vision and Pattern Recognition