Paper Info
Title
GASP, a generalized framework for agglomerative clustering of signed graphs and its application to Instance Segmentation
Abstract
We propose a theoretical framework that generalizes simple and fast algorithms for hierarchical agglomerative clustering to weighted graphs with both attractive and repulsive interactions between the nodes. This framework defines GASP, a Generalized Algorithm for Signed graph Partitioning 1 1 Code available at: https://github.com/abailoni/GASP, and allows us to explore many combinations of different linkage criteria and cannotlink constraints. We prove the equivalence of existing clustering methods to some of those combinations and introduce new algorithms for combinations that have not been studied before. We study both theoretical and empirical properties of these combinations and prove that some of these define an ultrametric on the graph. We conduct a systematic comparison of various instantiations of GASP on a large variety of both synthetic and existing signed clustering problems, in terms of accuracy but also efficiency and robustness to noise. Lastly, we show that some of the algorithms included in our framework, when combined with the predictions from a CNN model, result in a simple bottom-up instance segmentation pipeline. Going all the way from pixels to final segments with a simple procedure, we achieve state-of-the-art accuracy on the CREMI 2016 EM segmentation benchmark without requiring domain-specific superpixels.
Year
DOI
Venue
2022
10.1109/CVPR52688.2022.01135
IEEE Conference on Computer Vision and Pattern Recognition
Keywords
DocType
Volume
Segmentation,grouping and shape analysis, Medical,biological and cell microscopy
Conference
2022
Issue
Citations 
PageRank 
1
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Alberto Bailoni100.34
Constantin Pape212.04
Nathan Hütsch300.34
Steffen Wolf401.69
Thorsten Beier500.34
Anna Kreshuk685.02
Fred A. Hamprecht796276.24