二维类内最小交叉熵的图像分割快速方法

摘要

Enropy is acquired by calculating image's histogram,which only uses gray information of pixels.Minimum cross entropy is to search optimal threshold that can make minimum difference of information between original image and segmented image.On the basis of minimum cross entropy,this paper defines two-dimensional within-class minimum cross entropy by introducing spatial information of image.Moreover,this paper proposes an image segmentation method based on 2D within-class minimum cross entropy.Experimental results show that proposed method can obtain better segmentation effect than minimum cross entropy.In order to promote the speed of segmentation, corresponding recursive algorithm is proposed.Analysis shows that the computation time of 2D within-class minimum cross entropy method is reduced from more than 3 hours to several seconds.%熵是基于图像的一维灰度直方图得到的,仅利用了像素的灰度信息.最小交叉熵就是要寻找最优阈值使原始图像和分割图像之间的信息量的差异最小,在交叉熵的基础上,通过引入图像的空间信息,定义了二维类内交叉熵,并提出了基于二维类内最小交叉熵的图像分割方法.实验结果表明,充分利用图像的灰度信息和空间信息后,二维类内交叉熵取得了比交叉熵更好的分割效果.为了提高运算效率,提出了相应的快速递推算法,计算时间由从多于3小时减少到只要几秒.