A regional segmentation algorithm for nature sense images is presented in this paper. The method integrates two local edge cues and aims to reply the inadequacy in single visual cue based methods. Firstly

the logistic regression model is trained by 200 color images and the regression relationship between edge cues and image object boundary is established. Then

the model is employed to predict the boundary confidence of every pixel. Finally

the confidence functions are integrated into the procedure of mean shift segmentation by calculating the adaptive variable weights. Both quantitative and visual inspections operated in about 20 images show that the algorithm has better performance in suppressing over-segmentation and under-segmentation

and tightly localizing the boundaries.