Research on the adaptive probabilistic approach of texture analysis and its application in texture classification

Abstract

Within a Bayesian framework, Brady proposed the adaptive texture approach for more accurate description and applied this model in texture segmentation with a neighbourhood-based algorithm. In this paper, the efficiency of the texture model in Brady's segmentation method is investigated. In the segmentation experiments of Brodatz texture mosaics and a remote sensing image, the results show that the good segmentation performance mainly owes to the neighbourhood-based algorithm, but not Brady's texture description model. Moreover, this probabilistic model is applied in texture classification with a MAP method. To improve the correct classification rate of the image bank, a method combining the best adaptive texture description of each class is proposed and obviously improves the rate from 91% to 95%.