基于联合约束策略和稀疏表示的图像分割.

The interactive image segmentation algorithm based on pixel-level is sensitive to initial seed position and noise . However, the image segmentation method only oriented super-pixels is unable to retain image details so that under-segmentation could produce. Aiming at above problems, this paper proposed the segmentation model based on superpixel/pixel constraint and sparse representation. The method firstly defined two mutual constraint cost functions utilized the Gaussian model for super-pixels and pixels respectively. Meanwhile, it introduced constraint condition oriented on sparse decomposition to optimize the proposed model to avoid over-segmentation. Finally, this paper utilized joint optimization method to obtain the solution of the proposed model and estimated the object and background labels to realize object extraction. Experiment results demonstrate that the proposed method can obtain better segmentation results compared with the state-of-the-art methods and is more robust to noise. [ABSTRACT FROM AUTHOR]