Texture image segmentation based on wavelet-domain hidden Markov models

Many approaches have been used to segment texture based image, but can't have one's wish fulfilled. People unremittingly try to find high quality, more effective method. The 2-D discrete wavelet transform, as a powerful and effective approach. have got preferable production in image analyzing and image processing. However routine method focus on the assumption that the wavelet coefficients are independent and jointly gaussian. In fact. most real-world images, are not always gaussian distributed, there are underlying relationships and rules among these wavelet coefficients on both the same scale and the inter-scale. In this paper, we first reveal the dependencies among these coefficients through the wavelet-domain HAW's. then estimate the model parameters using the expectation maximization (EM) algorithms. The classification can be first realized through maximum likely method in each band, and then combine with the classification results of the three sub-bands from the 2-D wavelet transform and also integrate the classification results of different scale. These approaches offer improved segmentation accuracy.