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Our goal now is to clusterize images, such that images that are more similar are placed into the same cluster. The usability of this feature is the efficiently split the image space according to preferences. For example one user might want to split all images that have a main motif of a circle from images that have a main motif a line.
These algorithms are adapted such that the distance between the images is getting smaller. Kohonen needs this in order to self-organize the image space, and k-means needs to move the centroid closer to the images. In order to do so, one can use the genetic algorithms again to create mutations such that the resulting image is a real center ( k-means) or all the images get closer to each other (Kohonen). After the genetic modifications, the "cluster" is in fact an image who is an 'average' of it's cluster, according to distances in image space.
The results indicate that the best algorithm for this purpose seem to be Kohonen, which is not dependant on initial random pick (k-means) or classification algorithms (DBSCAN).