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== Graph partitioning methods ==
[[Graph (data structure)|Graph]] partitioning methods are an effective tools for image segmentation since they model the impact of pixel neighborhoods on a given cluster of pixels or pixel, under the assumption of homogeneity in images. In these methods, the image is modeled as a weighted, [[undirected graph]]. Usually a pixel or a group of pixels are associated with [[Vertex (graph theory)|nodes]] and [[Glossary of graph theory#Basics|edge]] weights define the (dis)similarity between the neighborhood pixels. The graph (image) is then partitioned according to a criterion designed to model "good" clusters. Each partition of the nodes (pixels) output from these algorithms are considered an object segment in the image; see [[Segmentation-based object categorization]]. Some popular algorithms of this category are normalized cuts,<ref>Jianbo Shi and [[Jitendra Malik]] (2000): [https://www.cs.cmu.edu/~jshi/papers/pami_ncut.pdf "Normalized Cuts and Image Segmentation"], ''IEEE Transactions on Pattern Analysis and Machine Intelligence'', pp 888–905, Vol. 22, No. 8</ref> [[random walker (computer vision)|random walker]],<ref>Leo Grady (2006): [http://vision.cse.psu.edu/people/chenpingY/paper/grady2006random.pdf "Random Walks for Image Segmentation"], ''IEEE Transactions on Pattern Analysis and Machine Intelligence'', pp. 1768–1783, Vol. 28, No. 11</ref> minimum cut,<ref>Z. Wu and R. Leahy (1993): [ftp://sipi.usc.edu/pub/leahy/pdfs/MAP93.pdf "An optimal graph theoretic approach to data clustering: Theory and its application to image segmentation"]{{
=== Markov random fields ===
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