Convolutional Neural Networks Learn Compact Local Image Descriptors
Conference paper
Abstract
We investigate if a deep Convolutional Neural Network can learn representations of local image patches that are usable in the important task of keypoint matching. We examine several possible loss functions for this correspondance task and show emprically that a newly suggested loss formulation allows a Convolutional Neural Network to find compact local image descriptors that perform comparably to state-of-the-art approaches.
Keywords
Convolutional Neural Networks Non-linear Dimensionality Reduction Local Image Descriptor LearningPreview
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