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arXiv · 2004.11498

Mining self-similarity: Label super-resolution with epitomic representations

Nikolay Malkin·Anthony Ortiz·Caleb Robinson·Nebojsa Jojic
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Abstract

We show that simple patch-based models, such as epitomes, can have superior performance to the current state of the art in semantic segmentation and label super-resolution, which uses deep convolutional neural networks. We derive a new training algorithm for epitomes which allows, for the first time, learning from very large data sets and derive a label super-resolution algorithm as a statistical inference algorithm over epitomic representations. We illustrate our methods on land cover mapping and medical image analysis tasks.

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Nikolay Malkin, Anthony Ortiz, Caleb Robinson, Nebojsa Jojic. 2020-04-24. Mining self-similarity: Label super-resolution with epitomic representations. https://arxiv.org/abs/2004.11498

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