@inproceedings{Jung_GCPR2022,
TITLE = {Optimizing Edge Detection for Image Segmentation with Multicut Penalties},
AUTHOR = {Jung, Steffen and Ziegler, Sebastian and Kardoost, Amirhossein and Keuper, Margret},
LANGUAGE = {eng},
ISBN = {978-3-031-16787-4},
DOI = {10.1007/978-3-031-16788-1_12},
PUBLISHER = {Springer},
YEAR = {2022},
DATE = {2022},
ABSTRACT = {The Minimum Cost Multicut Problem (MP) is a popular way for obtaining a graph<br>decomposition by optimizing binary edge labels over edge costs. While the<br>formulation of a MP from independently estimated costs per edge is highly<br>flexible and intuitive, solving the MP is NP-hard and time-expensive. As a<br>remedy, recent work proposed to predict edge probabilities with awareness to<br>potential conflicts by incorporating cycle constraints in the prediction<br>process. We argue that such formulation, while providing a first step towards<br>end-to-end learnable edge weights, is suboptimal, since it is built upon a<br>loose relaxation of the MP. We therefore propose an adaptive CRF that allows to<br>progressively consider more violated constraints and, in consequence, to issue<br>solutions with higher validity. Experiments on the BSDS500 benchmark for<br>natural image segmentation as well as on electron microscopic recordings show<br>that our approach yields more precise edge detection and image segmentation.<br>},
BOOKTITLE = {Pattern Recognition (DAGM GCPR 2022)},
EDITOR = {Andres, Bj{\"o}rn and Bernard, Florian and Cremers, Daniel and Frintrop, Simone and Goldl{\"u}cke, Bastian and Ihrke, Ivo},
PAGES = {182--197},
SERIES = {Lecture Notes in Computer Science},
VOLUME = {13485},
ADDRESS = {Konstanz, Germany},
}