Shape decomposition usually plays a significant role in shape analysis and its various applications. In this paper

we present a shape decomposition algorithm that combines the strength of skeleton and boundary features

which carry both global and local information of object shapes. In the proposed method

a bending potential ratio is introduced as a constraint to generate controllable decomposition results. Besides

the algorithm is able to avoid conglutination of important parts by fully utilizing the discrete curve evolution information on the boundary. Furthermore

the adopted robust skeleton method ensures noise insensitive decomposition results and avoids decomposing important parts into trivial ones. We choose the MPEG7 shape dataset and other traditional testing shapes as our experiment data. Experimental results show that our method satisfies subjective visual perception on shape decomposition and is robust to large shape noise.