Graph Partitioning Methods
Graph partitioning methods can effectively be used for image segmentation. In these methods, the image is modeled as a weighted, undirected graph. Usually a pixel or a group of pixels are associated with nodes and 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. Some popular algorithms of this category are normalized cuts, random walker, minimum cut, isoperimetric partitioning and minimum spanning tree-based segmentation.
Read more about this topic: Image Segmentation
Famous quotes containing the words graph and/or methods:
“When producers want to know what the public wants, they graph it as curves. When they want to tell the public what to get, they say it in curves.”
—Marshall McLuhan (1911–1980)
“If you want to know the taste of a pear, you must change the pear by eating it yourself.... If you want to know the theory and methods of revolution, you must take part in revolution. All genuine knowledge originates in direct experience.”
—Mao Zedong (1893–1976)