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Stochastic Relaxation on Partitions With Connected Components and Its Application to Image Segmentation
June 1998 (vol. 20 no. 6)
pp. 619-636

Abstract—We present a new method of segmentation in which images are segmented in partitions with connected components. We give computationally inexpensive algorithms for probability simulation and simulated annealing on the space of partitions with connected components of a general graph. In particular, Hastings algorithms and generalized Metropolis algorithms are defined to avoid heavy computation. To achieve segmentation, we propose a hierarchical approach which at each step minimizes a cost function on the space of partitions with connected components of a graph. The algorithm is applied to segment gray-level, color, and textured images.

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Index Terms:
Connected components, Gibbs distribution, simulated annealing, hierarchical segmentation, unsupervised segmentation, multiscale segmentation.
Citation:
Jia-Ping Wang, "Stochastic Relaxation on Partitions With Connected Components and Its Application to Image Segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 6, pp. 619-636, June 1998, doi:10.1109/34.683775
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