Oleg Zabluda's blog
Monday, September 19, 2016
 
Descriptor Matching with Convolutional Neural Networks: a Comparison to SIFT (2014) Philipp Fischer, Alexey...

Descriptor Matching with Convolutional Neural Networks: a Comparison to SIFT (2014) Philipp Fischer, Alexey Dosovitskiy, Thomas Brox
"""
descriptors like SIFT are not only used in recognition but also for many correspondence problems that rely on descriptor matching. [...] descriptors extracted from convolutional [...] neural networks perform consistently better than SIFT also in the low-level task of descriptor matching [...] unsupervised network slightly outperforms the supervised one.
[...]
Supervised [...] pre-trained model [...] follows Krizhevsky
[...]
We performed unsupervised training of a CNN as described in [5].
[...]
We extracted features from various layers of neural networks and measured their performance. While the unsupervised CNN clearly prefers features from higher network layers, the ImageNet CNN does not have a preference if the optimal patch size is used.
[...]
both neural nets perform much better than SIFT on all transformations except blur. The unsupervised network is superior to SIFT also on blur, but not by a large margin. [The unsupervised training explicitly considers blur, which is why the unsupervised CNN suffers less from it. ] Interestingly, the difference in performance between the networks and SIFT is typically as large as between SIFT and raw RGB [...] patches [...] as a weak ’naive’ baseline.
"""
http://arxiv.org/abs/1405.5769

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