Unsupervised Learning of Edges

Yin Li; Manohar Paluri; James M. Rehg; Piotr Dollár

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arXiv · 1511.04166

Unsupervised Learning of Edges

Yin Li·Manohar Paluri·James M. Rehg·Piotr Dollár

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Abstract

Data-driven approaches for edge detection have proven effective and achieve top results on modern benchmarks. However, all current data-driven edge detectors require manual supervision for training in the form of hand-labeled region segments or object boundaries. Specifically, human annotators mark semantically meaningful edges which are subsequently used for training. Is this form of strong, high-level supervision actually necessary to learn to accurately detect edges? In this work we present a simple yet effective approach for training edge detectors without human supervision. To this end we utilize motion, and more specifically, the only input to our method is noisy semi-dense matches between frames. We begin with only a rudimentary knowledge of edges (in the form of image gradients), and alternate between improving motion estimation and edge detection in turn. Using a large corpus of video data, we show that edge detectors trained using our unsupervised scheme approach the performance of the same methods trained with full supervision (within 3-5%). Finally, we show that when using a deep network for the edge detector, our approach provides a novel pre-training scheme for object detection.

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BibTeX RIS

Yin Li, Manohar Paluri, James M. Rehg, Piotr Dollár. 2015-11-13. Unsupervised Learning of Edges. https://arxiv.org/abs/1511.04166

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