Abstract
An adaptive image segmentation method using Markov random field model for suburban aerial images is presented in this paper. The image is modelled as a collection of regions characterised by slowly moving averages and standard deviation. Decreasing sized windows are used to calculate the moving averages during the iteration process. A function based weighting parameter between the two components in the energy function is also used to improve the performance of unsupervised segmentation. A hierarchical implementation scheme is also introduced to reduce the computation load and increase the segmentation speed.
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