Aiming at the problem of image segmentation,a new image thresholding method based on Gaussian hybrid model and relative entropy measure is presented.In the proposed method,the problem of image segmentation is seen as a matching problem between two probability vectors.The gray level distribution of image histogram is fitted by Gaussian hybrid model firstly.Then the difference between fitting distribution and gray level distribution of original image is measured by relative entropy measure,and the measurement is used as the criterion function for image thresholding.When the image is segmented,the optimal threshold is obtained by minimizing the criterion function in the range of gray levels.For demonstrating the effectiveness of the proposed method,the experiments on NDT,SAR and infrared images are carried out and the performances of the proposed method is compared with those of three famous tradition methods and two state-of-the-art methods.The experiment results indicate that the proposed method can obtain better segmentation results,and this method is an effective approach for image segmentation.