Supervised textured image segmentation using feature smoothing and probabilistic relaxation techniques

A description is given of a supervised textured image segmentation algorithm that provides improved segmentation results. An improved method for extracting textured energy features in the feature extraction stage is described. It is based on an adaptive noise smoothing concept that takes the nonstationary nature of the problem into account. Texture energy features are first estimated using a window of small size to reduce the possibility of mixing statistics along region borders. The estimated texture energy feature values are smoothed by a quadrant filtering method to reduce the variability of the estimates while retaining the region border accuracy. The estimated feature values of each pixel are used by a Bayes classifier to make an initial probabilistic labeling. The spatial constraints are enforced through the use of a probabilistic relaxation algorithm. Two probabilistic relaxation algorithms are investigated. Limiting the probability labels by probability threshold is proposed. The tradeoff between efficiency and degradation of performed is studied