Region-based hierarchical image representation is crucial in many computer vision applications. However, in practice, an image hierarchy is usually dense, and contains many less informative branches. It is expected that a hierarchy should be accurate and simplified, which is not only desirable for different applications, but also saves considerable computational load for the further analysis. To achieve this target, this paper proposes a novel approach for unsupervised simplification of region-based image hierarchies, which employs the global and local evolution analyses of a hierarchy. First, we introduce a global evolution analysis in the scale-sets framework, which provides clues for eliminating less informative branches. Moreover, a hybrid unsupervised simplification method is designed, utilizing the information from global and local evolution functions. A number of experiments on various images have shown that the proposed approach is effective and efficient in removing less informative nodes (averagely about 90% of the whole nodes), while preserving salient image details and retaining the accuracy.