Extensions of Karger's Algorithm: Why They Fail in Theory and How They Are Useful in Practice
Abstract
The minimum graph cut and minimum $s$$t$cut problems are important primitives in the modeling of combinatorial problems in computer science, including in computer vision and machine learning. Some of the most efficient algorithms for finding global minimum cuts are randomized algorithms based on Karger's groundbreaking contraction algorithm. Here, we study whether Karger's algorithm can be successfully generalized to other cut problems. We first prove that a wide class of natural generalizations of Karger's algorithm cannot efficiently solve the $s$$t$mincut or the normalized cut problem to optimality. However, we then present a simple new algorithm for seeded segmentation / graphbased semisupervised learning that is closely based on Karger's original algorithm, showing that for these problems, extensions of Karger's algorithm can be useful. The new algorithm has linear asymptotic runtime and yields a potential that can be interpreted as the posterior probability of a sample belonging to a given seed / class. We clarify its relation to the random walker algorithm / harmonic energy minimization in terms of distributions over spanning forests. On classical problems from seeded image segmentation and graphbased semisupervised learning on image data, the method performs at least as well as the random walker / harmonic energy minimization / Gaussian processes.
 Publication:

arXiv eprints
 Pub Date:
 October 2021
 DOI:
 10.48550/arXiv.2110.02750
 arXiv:
 arXiv:2110.02750
 Bibcode:
 2021arXiv211002750J
 Keywords:

 Computer Science  Data Structures and Algorithms;
 Computer Science  Computer Vision and Pattern Recognition;
 Computer Science  Machine Learning;
 Statistics  Machine Learning
 EPrint:
 Oral at ICCV 2021