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Unsupervised image segmentation using a simple MRF model with a new implementation scheme

TitleUnsupervised image segmentation using a simple MRF model with a new implementation scheme
Publication TypeJournal Article
Year of Publication2004
AuthorsDeng, H., and D. A. Clausi
JournalPattern Recognition
Volume37
Pagination2323–2335
KeywordsColor Image, Expectation-maximization (EM) algorithm, Feature space, image segmentation, K-means clustering, Markov random field (MRF), sea ice, synthetic aperture radar (SAR), Texture Image, unsupervised segmentation
Abstract

A simple Markov random field model with a new implementation scheme is proposed for unsupervised image segmentation based on image features. The traditional two-component MRF model for segmentation requires training data to estimate necessary model parameters and is thus unsuitable for unsupervised segmentation. The new implementation scheme solves this problem by introducing a function-based weighting parameter between the two components. Using this method, the simple MRF model is able to automatically estimate model parameters and produce accurate unsupervised segmentation results. Experiments demonstrate that the proposed algorithm is able to segment various types of images (gray scale, color, texture)and achieves an improvement over the traditional method.