Learning textural concepts through multilevel symbolic transformations

Author

Bala, Jerzy W. ; Michalski, Ryszard S.

Author_Institution

Center for Artificial Intelligence, George Mason Univ., Fairfax, VA, USA

fYear

1991

fDate

10-13 Nov 1991

Firstpage

100

Lastpage

107

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

Keywords

computerised pattern recognition; computerised picture processing; knowledge based systems; learning systems; TEXTRAL system; computerised pattern recognition; convolutional operators; knowledge based system; rule assertions; rule learning; symbolic transformations; textural image concept learning; Acoustic noise; Artificial intelligence; Character recognition; Computer vision; Data mining; Humans; Image generation; Image recognition; Labeling; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Tools for Artificial Intelligence, 1991. TAI '91., Third International Conference on

Conference_Location

San Jose, CA

Print_ISBN

0-8186-2300-4

Type

conf

DOI

10.1109/TAI.1991.167081

Filename

167081