A fast learning algorithm for image segmentation with max-pooling convolutional networks

Jonathan Masci, Alessandro Giusti, Dan Ciresan, Gabriel Fricout, Jurgen Schmidhuber

Research output: Chapter in Book/Report/Conference proceedingConference contribution

48 Scopus citations

Abstract

We present a fast algorithm for training MaxPooling Convolutional Networks to segment images. This type of network yields record-breaking performance in a variety of tasks, but is normally trained on a computationally expensive patch-by-patch basis. Our new method processes each training image in a single pass, which is vastly more efficient. We validate the approach in different scenarios and report a 1500-fold speed-up. In an application to automated steel defect detection and segmentation, we obtain excellent performance with short training times. © 2013 IEEE.
Original languageEnglish (US)
Title of host publication2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
PublisherIEEE Computer Society
Pages2713-2717
Number of pages5
ISBN (Print)9781479923410
DOIs
StatePublished - Jan 1 2013
Externally publishedYes

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