| Image segmentation is one of the hot and difficult issues in the field of computer vision and image analysis.The goal of the image segmentation is to partition a given image into several non-overlapped regions according to some similarity characteristics(texture,intensity and color,etc.).Images are different,so there are many kinds of image segmentation methods.Recently,two-stage image segmentation methods have been widely paid attention by many researchers,due to their flexible structure and excellent performance.Generally speaking,the first stage is to find a smoothed image that can facilitate the segmentation,and the second stage is to segment the smoothed image.This paper proposes a two-stage segmentation method.The first stage proposes a novel discrete smoothing model for the images including texture,noise and blur.This model uses a sparse regularization(RTV,relative total variation)instead of the TV(Total variation)regularization in the model proposed by Cai.et al.in [SIAM J.Imaging Sciences,2013,6(1):368-390].This sparse regularization can highlight the edge of target in image very well.The second stage,the segmentation is done using the K-means clustering method,which has many advantages over other clustering segmentation methods.Because our model is not convex,we decompose the RTV measure into two nonlinear term and a quadratic term.The advantage is that the problem with the non-linear part can be transformed to solving a series of linear equation systems.Experimental results show the effectiveness of our method.Finally,we discuss the selection of the parameters for the proposed algorithm. |
