Memory colours refer to the colour of specific image regions that have the essential attribute of being perceived in a consistent manner by human observers. In colour correction-or rendering-tasks, this consistency implies that they have to be faithfully reproduced; their importance, in that respect, is greater than for other regions in an image. There are various schemes and attributes to detect memory colours, but the preferred method remains to segment the images into meaningful regions, a task for which many algorithms exist. Memory colour regions are not, however, similar in their attributes. Significant variations in shape, size, and texture exist. As such, it is unclear whether a single segmentation algorithm is the most adapted for all of these classes. Using a large database of real-world images, we calculate class-specific geometrical features, eigenregions, that can be used to evaluate how well an algorithm is adapted to segment a given class and give a measure of localisation of memory colours. We also compare the performance of our class-specific eigenregions to general ones in the task of memory colour region classification and observe that they provide a noticeable improvement in classification rates.