Title :
Scene understanding by rule evaluation
Author :
Blaschof, W.F. ; Caelli, Terry
Author_Institution :
Dept. of Psychol., Alberta Univ., Edmonton, Alta., Canada
fDate :
11/1/1997 12:00:00 AM
Abstract :
We consider how machine learning can be used to help solve the problem of identifying objects or structures composed of parts in complex scenes. We first discuss a conditional rule generation technique that is designed to describe structures using part attributes and their relations. We then show how the resultant rules can be used for region labeling and examine constraint propagation techniques for improving rule-based object classification
Keywords :
constraint handling; image classification; learning (artificial intelligence); object recognition; complex scenes; conditional rule generation technique; constraint propagation techniques; machine learning; part attributes; region labeling; rule evaluation; rule-based object classification; scene understanding; Computer vision; Decision trees; Labeling; Layout; Logic programming; Machine learning; Object detection; Object recognition; Pattern recognition; Signal generators;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
