Learning textural concepts through multilevel symbolic transformations

Abstract

The TEXTRAL system, used for determining structural visual properties of textures through symbolic transformations, is presented. The method consists of two phases: one that extracts information from raw textural images by applying convolutional operators and learns an initial set of rules; and a second that iteratively extracts symbolic information from the transformed representation of initial image and learns another set of rules. The transformed symbolic representation is obtained by applying previously learned rules to a new image location and generating symbolic images based on rule assertions.<>