Multi-scale pseudo labeling for unsupervised deep edge detection

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Deep learning currently rules edge detection. However, the impressive progress heavily relies on high-quality manually annotated labels which require a significant amount of labor and time. In this study, we propose a novel unsupervised learning framework for deep edge detection. It adopts a gradient-based method to generate scale-dependent pseudo edge maps, which match with the hierarchical structure of deep networks. It leverages both the representation learning capability of deep learning, and the simplicity of traditional methods. Experiments on three popular data sets show that the proposed method can suppress non-object edges and reduce the gap with its supervised counterpart due to the introduction of information of various scales and smoothing strategy.

Original languageEnglish
Article number111057
JournalKnowledge-Based Systems
Number of pages15
Publication statusPublished - 2023

Bibliographical note

Publisher Copyright:
© 2023 The Author(s)

    Research areas

  • Edge detection, Multi-scale modeling, Pseudo labeling, Unsupervised learning

ID: 372609481