We introduce a new image texture segmentation algorithm, HMTseg, based on w
avelets and the hidden Markov tree (HMT) model. The HMT is a tree-structure
d probabilistic graph that captures the statistical properties of the coeff
icients of the wavelet transform. Since the HMT is particularly well suited
to images containing singularities (edges and ridges), it provides a good
classifier for distinguishing between textures. Utilizing the inherent tree
structure of the wavelet EMT and its fast training and likelihood computat
ion algorithms, we perform texture classification at a range of different s
cales. We then fuse these multiscale classifications using a Bayesian proba
bilistic graph to obtain reliable final segmentations. Since HMTseg works o
n the wavelet transform of the image, it can directly segment wavelet-compr
essed images without the need for decompression into the space domain. We d
emonstrate the performance of HMTseg with synthetic, aerial photo, and docu
ment image segmentations.