SVRF is a CRF based extension for SVM (Chi-Hoon et al.
SVRF may be mathematically represented using equation (2) where O([y.sub.i], i(X)) is an SVM-based Observation-Matching potential and V([y.sub.i], [y.sub.j], X) is a (modified) DRF pair wise potential:
SVRF is used along with the kernel functions to implement effective segmentation augmented with contextual knowledge.
Mixture density kernels are used to integrate an adaptive kernel strategy to the SVRF based clustering as they facilitate learning of kernels directly from image data rather than using a static approach.
In the validation phase, SVRF uses training samples to classify the input image where tone, texture and CA rules are adopted for an effective approach.
Comparative analyses of the areal extents also indicate that the SVRF approach yields better results compared to the other methods.
3) and visual interpretation also reveals the accuracy of SVRF based methodology.