In order to remedy some of the disadvantages of trainable sequential maximum a posterior (TSMAP)

we introduce an adaptive trainable sequential maximum a posterior (ATSMAP) approach for image segmentation based on multiscale Markov model.This method obtains the ground truth segmentations at all scales by wavelet decomposition of the accurate segmentation of the original image

and then estimates the parameters of multiscale quadtree models.Also

the parameters of the context model are estimated by a simple way that estimates the parameters with the low dimension feature instead of the context itself.Compared with TSMAP

the advantages of ATSMAP are as follows:1).The parameters of quadtree can be retrained for the specific image being segmented.2) The estimation of parameters for context models is very computationally efficient.The experimental results of the segmentation for bothsynthetic images and SAR images indicate that the approach fairly improves the segmentation accuracy over TSMAP and some other multiscale Markov based methods.