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Unsupervised Feature Learning for Dense Correspondences across Scenes
[article]
2015
arXiv
pre-print
We propose a fast, accurate matching method for estimating dense pixel correspondences across scenes. It is a challenging problem to estimate dense pixel correspondences between images depicting different scenes or instances of the same object category. While most such matching methods rely on hand-crafted features such as SIFT, we learn features from a large amount of unlabeled image patches using unsupervised learning. Pixel-layer features are obtained by encoding over the dictionary,
arXiv:1501.00642v2
fatcat:tmrxi23wpvblxfyofpxyhlccoe