Abstract-Multivariate image segmentation is a challenging task, influenced by large intraclass variation that reduces class distinguishability as well as increased feature space sparseness and solution space complexity that impose computational cost and degrade algorithmic robustness. To deal with these problems, a Markov random field (MRF) based multivariate segmentation algorithm called "multivariate iterative region growing using semantics" (MIRGS) is presented. In MIRGS, the impact of intraclass variation and computational cost are reduced using the MRF spatial context model incorporated with adaptive edge penalty and applied to regions. Semantic region growing starting from watershed over-segmentation and performed alternatively with segmentation gradually reduces the solution space size, which improves segmentation effectiveness. As a multivariate iterative algorithm, MIRGS is highly sensitive to initial conditions. To suppress initialization sensitivity, it employs a region-level k-means
REF presented a MRF based multivariate segmentation algorithm called "multivariate iterative region growing using semantics" (MIRGS): the impact of interclass variation and computational cost are reduced using the MRF spatial context model incorporated with adaptive edge penalty and applied to regions.
Multivariate Image Segmentation Using Semantic Region Growing With Adaptive Edge Penalty
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Mathematics Medicine Computer Science