Affiliations: [a] Department of Computer Science and Technology, Harbin Institute of Technology, China
| [b] School of Mechanical, Electrical and Information Engineering, Shandong University, China
| [c] Department of Integrative Biology, University of Texas at Austin, USA
| [d]
University of Pittsburgh, Pittsburgh, PA, USA
Correspondence:
[*]
Corresponding author. Dong Li, School of Mechanical, Electrical and Information Engineering, Shandong University, China. E-mail: dongli@sdu.edu.cn.
Abstract: We present a novel graph cut method for iterated segmentation of objects with specific shape bias (SBGC). In contrast with conventional graph cut models which emphasize the regional appearance only, the proposed SBGC takes the shape preference of the interested object into account to drive the segmentation. Therefore, the SBGC can ensure a more accurate convergence to the interested object even in complicated conditions where the appearance cues are inadequate for object/background discrimination. In particular, we firstly evaluate the segmentation by simultaneously considering its global shape and local edge consistencies with the object shape priors. Then these two cues are formulated into a graph cut framework to seek the optimal segmentation that maximizing both of the global and local measurements. By iteratively implementing the optimization, the proposed SBGC can achieve joint estimation of the optimal segmentation and the most likely object shape encoded by the shape priors, and eventually converge to the candidate result with maximum consistency between these two estimations. Finally, we take the ellipse shape objects with various segmentation challenges as examples for evaluation. Competitive results compared with state-of-the-art methods validate the effectiveness of the technique.
