Clustering stability for automated color image segmentation

Clustering is a well-established technique for segmentation. However, clustering validation is rarely used for this purpose. In this work we adapt a clustering validation method, Clustering Stability (CS), to automatically segment images. CS is not limited by image dimensionality nor by the clustering algorithm. We show clustering and validation acting together as a data-driven process able to find the optimum number of partitions according to our proposed color-texture feature representation. We also describe how to adapt CS to detect the best settings required for feature extraction. The segmentation solutions found by our method are supported by a stability score named STI, which provides an objective quantifiable metric to obtain the final segmentation results. Furthermore, the STI allows to compare multiple alternative solutions and select the most appropriate according to the index meaning. We successfully test our procedure on texture and natural images, and 3D MRI data.