General region merging approaches based on information theory statistical measures

This work presents a new statistical approach to region merging where regions are modeled as arbitrary discrete distributions, directly estimated from the pixel values. Under this framework, two region merging criteria are obtained from two different perspectives, leading to information theory statistical measures: the Kullback-Leibler divergence and the Bhattacharyya coefficient. The developed methods are size-dependent, which assures the size consistency of the partitions but reduces their size resolution. Thus, a size-independent extension of the previous methods, combined with a modified merging order, is also proposed. Additionally, an automatic criterion to select the most statistically significant partitions from the whole merging sequence is presented. Finally, all methods are evaluated and compared with other state-of-the-art region merging techniques.