Multiresolution Algorithm for Image Segmentation Using MRMRF with Edge Information

This paper proposes an image segmentation algorithm based on the multiresolution Markov Random Fields (MRMRF) model by incorporating edge information. The discrete wavelet transform (DWT) is utilized as a multiresolution technique to obtain feature information at multiple scales and provide computational efficiency. On each scale, the low-frequency wavelet coefficients are employed as image features, while the high-frequency ones are served as computing the edge strength, which is embedded in the label field modeling and makes our algorithm more adaptive to image contents. At the primary scale, there are no high-frequency wavelet coefficients to be used, and the edge strength is calculated by image gradients both on horizontal and vertical directions. Image segmentation is successively carried out with MAP criterion from the coarsest resolution to the finest one, and this algorithm has been successfully tested both on synthetic and remote sensed images.