Hierarchical Image Segmentation based on Iterative Contraction and Merging

Professorprofessor Sheng-Jyh Wang
ApplicationMedicine and health care, Surveillance and security, and Vehicle
Technical BenefitDecrease error rate / improve stability and Improve efficiency/ increasing execution speed
Technology StatusCan be transferred


In this technology, we propose a new framework for hierarchical image segmentation based on iterative contraction and merging (ICM). In the proposed framework, we treat the hierarchical image segmentation problem as a sequel of optimization problems, with each optimization process being realized by a contraction-and-merging process to identify and merge the most similar data pairs at the current resolution. At the beginning, we perform pixel-based contraction and merging to quickly combine image pixels into initial region-elements with visually indistinguishable intra-region color difference. After that, we iteratively perform region-based contraction and merging to group adjacent regions into larger ones to progressively form a segmentation dendrogram for hierarchical segmentation. Comparing to the state-of-the-art techniques, the proposed algorithm can not only produce high-quality segmentation results in a more efficient way, but also keep a lot of boundary details in the segmentation results.

Figure 1. Sampled merging results of the ICM process

demo video:

Spec. request

platform :

Window or Linux


Matlab & C/C++

system requirment:

Ram: more than 2GB