A Random Walks View of Spectral Segmentation
نویسندگان
چکیده
We present a new view of clustering and segmentation by pairwise similarities. We interpret the similarities as edge ows in a Markov random walk and study the eigenvalues and eigenvectors of the walk's transition matrix. This view shows that spectral methods for clustering and segmentation have a probabilistic foundation. We prove that the Normalized Cut method arises naturally from our framework and we provide a complete characterization of the cases when the Normalized Cut algorithm is exact. Then we discuss other spectral segmentation and clustering methods showing that several of them are essentially the same as NCut.
منابع مشابه
Learning Segmentation by Random Walks
The context here is image segmentation because it was in this domain that spectral clustering was introduced by Shi and Malik in 2000. Meila and Shi provide a random-walk interpretation of the spectral clustering algorithm, and then use a transition probability matrix to create a model which learns to segment images based on pixel intensity (which they call “edge strength”) and “co-circularity”...
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