Learning Segmentation by Random Walks
Abstract
We present a new view of image segmentation by pairwise simi(cid:173) larities. We interpret the similarities as edge flows in a Markov random walk and study the eigenvalues and eigenvectors of the walk's transition matrix. This interpretation shows that spectral methods for clustering and segmentation have a probabilistic foun(cid:173) dation. In particular, we prove that the Normalized Cut method arises naturally from our framework. Finally, the framework pro(cid:173) vides a principled method for learning the similarity function as a combination of features.
Cite
Text
Meila and Shi. "Learning Segmentation by Random Walks." Neural Information Processing Systems, 2000.Markdown
[Meila and Shi. "Learning Segmentation by Random Walks." Neural Information Processing Systems, 2000.](https://mlanthology.org/neurips/2000/meila2000neurips-learning/)BibTeX
@inproceedings{meila2000neurips-learning,
title = {{Learning Segmentation by Random Walks}},
author = {Meila, Marina and Shi, Jianbo},
booktitle = {Neural Information Processing Systems},
year = {2000},
pages = {873-879},
url = {https://mlanthology.org/neurips/2000/meila2000neurips-learning/}
}