Boundary Learning by Optimization with Topological Constraints

Recent studies have shown that machine learning can improve the accuracy of detecting object boundaries in images. In the standard approach, a boundary detector is trained by minimizing its pixel-level disagreement with human boundary tracings. This naive metric is problematic because it is overly sensitive to boundary locations. This problem is solved by metrics provided with the Berkeley Segmentation Dataset, but these can be insensitive to topological differences, such as gaps in boundaries. Furthermore, the Berkeley metrics have not been useful as cost functions for supervised learning. Using concepts from digital topology, we propose a new metric called the warping error that tolerates disagreements over boundary location, penalizes topological disagreements, and can be used directly as a cost function for learning boundary detection, in a method that we call Boundary Learning by Optimization with Topological Constraints (BLOTC). We trained boundary detectors on electron microsco...

• Type: Conference
• Year: 2010
• Where: CVPR
• Authors: Viren Jain, Benjamin Bollmann, Bobby Kasthuri, Ken Hayworth, Richard Schalek, Juan Carlos Tapia, Daniel Berger, Mark Richardson, Kevin Briggman, Moritz Helmstaedter, Winfried Denk, Jeff Lichtman, Sebastian Seung
• Keywords: Boundary Detectors, Boundary Location, Computer Vision, CVPR 2010, Human Boundary Tracings