Classification and segmentation of rotated and scaled textured images using texture “tuned” masks

摘要

A rotation and scale invariant texture classifier function is described for effective classification and segmentation of images involving textures of unknown rotation and scale changes. The classifier used is the texture energy associated with a mask that has been “tuned” to be both discriminant between different textures, and to be invariant to rotation and scale changes. The mask tuning scheme utilized is based on task-oriented criterion optimization via a guided random search procedure to incorporate the changes. Both a dynamic texture sample set using a two-dimensional (2D) linked list and a re-ranking procedure are applied for training. Maximum feature dispersion of inter texture classes and high feature convergence of inner texture class samples associated with other statistical measures are suggested as key criteria in training. In a study based on 15 distinct Brodatz textures it is found that: the tuning process although computationally intensive converges efficiently; the mean classifier values of the classifier for a particular texture at different orientation and different scales are tightly clustered. An objective measure of classification capability is determined by computing the standard deviation of the classifier over pure texture at definite orientation and scale. Examples are presented of the classifier function applied to the segmentation of collages of Brodatz textures, comprising regions of various orientation and scale.