Interactive Image Segmentation with Conditional Random Fields
Xiaowei Geng, Jieyu Zhao · 2008
A novel image segmentation method using conditional random fields is presented in this paper. It dynamically fuses color, texture, spatial and edge information to implement image segmentation associated with interactive inputs. Most existing algorithms combine multiple cues statically with a constant ratio, it is hard for them to describe the inherent property of different images efficiently. The proposed method takes the full advantage of the user labeled information about the foreground and background, and builds a certain standard to measure the reliability of established probability distribution, with which to assemble energy terms in the conditional random field. This makes the related energy terms fusing dynamically in accordance with the image inner property, and improves the segmentation power of the model. From experiments we find that the conditional random field is able to capture the image edge characteristics and produce meaningful outputs. These results demonstrate that this method performs steady and works well on various natural images.