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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Bibliographic Details

Published in:	2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 4353 - 4361
Main Authors:	Zagoruyko, Sergey, Komodakis, Nikos
Format:	Conference Proceeding Journal Article
Language:	English
Published:	IEEE 01.06.2015
Subjects:	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
Online Access:	Get full text
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