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Learning to compare image patches via convolutional neural networks
2015
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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-based model that is trained to account for a wide variety of changes in image appearance. To that end, we explore and study multiple neural network architectures, which are specifically adapted to this
doi:10.1109/cvpr.2015.7299064
dblp:conf/cvpr/ZagoruykoK15
fatcat:vpwke4ad6bhixg2puzbdbw4zhq