The global criterion is used to identify significant changes in the segmentation results from one iteration to the next. This criterion is same as the dissimilarity criterion, except that it compares the original image data with the region mean image from the current segmentation. The value of this criterion is calculated at each image point and averaged over the entire image. The global criterion is also used to select which region is to be split in the final, backtracking stage of the algorithm. In this case the value of this criterion is calculated at each image point in the region under consideration, and is averaged over that region.
Larger values result in longer execution times.
For the same data sets, we have also found reasonable values for convfact to be in the range of 1.001 to 1.010. 4 and 5 constitute the spectral clustering portion. These region growing and spectral clustering steps are iterated until two or fewer regions remain. In order to make viewing of the results more convenient, the region labels can be renumbered from largest region to the smallest. This hybrid image segmentation algorithm is very computationally intensive.