A graph distance measure for image analysis

Abstract

Attributed relational graphs (ARGs) have shown superior qualities when used for image representation and analysis in computer vision systems. A new, efficient approach for calculating a global distance measure between attributed relational graphs is proposed, and its applications in computer vision are discussed. The distance measure is calculated by a global optimization algorithm that is shown to be very efficient for this problem. The approach shows good results for practical size ARGs. The technique is also suitable for parallel processing implementation.