Improving image segmentation with evidence transfer

Summary

The aim of this dissertation is to examine the use of Evidence Transfer to improve image segmentation. Evidence transfer is a transfer learning technique that makes use of additional sources of data as external evidence from an unknown auxiliary task to improve the performance of the model in the primary task. We use scribbles of the buildings as external evidence with this method to improve segmentation of skyline images.