Accuracy Assessment Measures for Object-based Image Segmentation Goodness

To select on image segmentation from sets of segmentation results. measures for ranking the segmentations relative to a set Of reference objects are needed We review selected vector-based measures designed to compare the results object-based image segmentation with sets of training objects extracted from the image of interest We describe and compare area-based and location-based measures that measure the shape similarity between segments and training objects By implementing the measures in two object-based image processing, software packages, we illustrate their Use in terms of automatically identifying parsimonious parameter combinations from arbitrarily large sets of segmentation results. The results show that the measures have have divergent performance in terms of the identification of parameter combinations Clusterin,, of the results in measure space narrows the search We illustrate combination schemes for the measures for generating rankings of segmentation results The raked segmentation results are illustrated and described.