Learning Segmentation by Random Walks
Conference Paper, Proceedings of (NeurIPS) Neural Information Processing Systems, pp. 873 - 879, November, 2001
Abstract
We present a new view of image segmentation by pairwise similarities. 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 foundation. In particular, we prove that the Normalized Cut method arises naturally from our framework. Finally, the framework provides a principled method for learning the similarity function as a combination of features.
BibTeX
@conference{Maila-2001-16799,author = {Marina Maila and Jianbo Shi},
title = {Learning Segmentation by Random Walks},
booktitle = {Proceedings of (NeurIPS) Neural Information Processing Systems},
year = {2001},
month = {November},
pages = {873 - 879},
}
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