Region merging parameter dependency as information diversity to create sparse hierarchies of partitions

Region merging techniques usually include parameters that may be used to optimize or adapt the algorithm to a specific image type. Although, an appropriate tuning may provide a significant improvement, it also introduces a severe performance dependency on the parameter setting. The goal of this work is to transform the parameter dependency into an increase of accuracy and stability of the segmentation results. The idea is to use different parameter settings as specific type of diversity in an information fusion process based on a cooperative region merging approach. The potential of this parameter removal strategy is objectively evaluated on a set of state-of-the-art information theoretical region merging techniques for the removal of parameters: (i) in the region model, and (ii) in the merging order.