A fast region-based image segmentation based on least square method

Image segmentation is always very important for computer vision and pattern recognition. Moreover, how to fast extract objects from a given image is still a problem for real time image processing. Most of the traditional region-based models depend on global information to converge to minimum error segmentation, but they are always time-consuming, and result in no effective segmentation. In this paper, we propose a region-based model with weight matrix to detect objects fast based on least square method. The basic ideal of our model is to build up a minimum error functional by approximating objects and background of original image with two constants respectively. At the same time, we introduce a weight matrix into the region-based model, which can enhance the weight of objects while reducing the influence from background. Our method can fast converge through alternating iterations under least square method. We also compare it with other region-based methods to show the improvements that can be achieved. Experimental results show the advantages of our method in terms of efficiency in image segmentation without losing accuracy.