Sections
You are here: Home ICG Publications Interactive Multi-Label Segmentation

Interactive Multi-Label Segmentation

Authors Santner Jakob, Pock Thomas, Bischof Horst
Appeared in Asian Conference on Computer Vision (ACCV)
Date  2010
Abstract This paper addresses the problem of interactive multi-label segmentation. We propose a powerful new framework using several color models and texture descriptors, Random Forest likelihood estimation as well as a multi-label Potts-model segmentation. We perform most of the calculations on the GPU and reach runtimes of less than two seconds, allowing for convenient user interaction. Due to the lack of an interactive multi-label segmentation benchmark, we also introduce a large publicly available dataset. We demonstrate the quality of our framework with many examples and experiments using this benchmark dataset.
Link LINK
[Powered by Plone]