Color image simplification by morphological hierarchical segmentation and color clustering

Morphological hierarchical segmentation of color images may be achieved in a straightforward way by measuring the persistence of regional minima from color gradients and using these measurements as a criterion to select markers for the watershed from markers framework. Since color has an implicit role in the selection of markers, the segmentation process may provide a bad combination of distinct colored regions, and this may lead to a distorted image simplification. This paper proposes a new method to color image simplification in which the importance of color is raised because color information is added to the marker selection process. Such method provides finer control over the final number of regions (n) and the resulting number of colors (c). A color clustering method splits the regional minima in to c minima sets, each of which has a representative color. The most prominent regional minima from each minima set are selected to form the markers for the segmentation framework. In the final segmentation, the color assigned to a region is given by the representative color bound to the marker that points to the region. It leads to an image whose segmented regions are quantized to fewer distinct colors.