Learning Segmentation by Random Walks
نویسندگان
چکیده
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”. The main contribution claimed, however, is “showing that spectral segmentation methods have a probabilistic foundation.”
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