基于第二代Bandelet域隐马尔可夫树模型的图像分割

Image Segmentation Using Second Generation Bandelet-domain Hidden Markov Tree Models

第二代Bandelet可以充分利用图像的内在几何正则性特点,并能自适应获得图像的最优表示.本文采用隐马尔可夫树(Hidden Markov tree,HMT)模型对图像的第二代Bandelet系数建模,通过多尺度参数训练和基于上下文的最大后验概率进行图像分割.为了评价本文方法的性能,我们分别选择合成纹理图像、航拍图像和SAR图像进行实验,并与小波域HMT模型分割方法(WD-HMTseg)和Contourlet域HMT模型分割方法(CHM Tseg)进行比较说明算法的有效性.实验结果表明本文方法不但在边缘准确性和区域一致性上有明显改进,而且也降低了纹理图的错分概率.

Second generation bandelet can make full use of intrinsic geometric regularity and provide optimal image representation adaptively. In this paper, we modeled the second generation bandelet coefficients of an image using hidden Markov tree （HMT） model and obtained the image segmentation results using multiscale parameter training and contextbased maximum posterior probability. In order to evaluate the performance of the proposed method, we made experiments on synthetic mosaic images, aerial images, and SAR images. The segmentation results were compared with the waveletdomain HMTseg （WD-HMTseg） one and contourlet-domain HMTseg （CHMTseg） one. Experiment results showed that our method not only obtained more exact boundary and uniform regions, but also reduced the misclassification rate of texture images.