Segmentation of textured images using a multiple resolution approach

A method is presented for segmenting images into a discrete set of classes by first segmenting at low resolution and then progressing to finer resolutions until individual pixels are classified. This multiple resolution method results in accurate segmentations and requires significantly less computation than some previously known methods. The segmentation algorithm used at each resolution is based on maximum a posteriori estimation of the field of pixel classifications, which is modeled as a Markov random field. The maximization is performed by a deterministic greedy algorithm which iteratively chooses the classification of individual pixels or blocks of pixels. A texture model is also developed which allows the extraction of a texture statistic for each pixel and is well suited for use with the proposed algorithm. Measurements of algorithm performance under varying conditions of region size and signal-to-noise ratio are presented