Learning to compare image patches via convolutional neural networks

In this paper we show how to learn directly from image data (i.e., without resorting to manually-designed features) a general similarity function for comparing image patches, which is a task of fundamental importance for many computer vision problems. To encode such a function, we opt for a CNN-base...

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Vydané v:	2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) s. 4353 - 4361
Hlavní autori:	Zagoruyko, Sergey, Komodakis, Nikos
Médium:	Konferenčný príspevok.. Journal Article
Jazyk:	English
Vydavateľské údaje:	IEEE 01.06.2015
Predmet:	Adaptation models Benchmark testing Computational modeling Computer architecture Computer vision Conferences Convolutional codes Mathematical analysis Mathematical models Neural networks Pattern recognition Tasks Training
ISSN:	1063-6919, 1063-6919
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