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 language||English (US)|
|Title of host publication||2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings|
|Publisher||IEEE Computer Society|
|Number of pages||5|
|State||Published - Jan 1 2013|