Title :
A Metric for Comparing Relational Descriptions
Author :
Shapiro, Linda G. ; Haralick, Robert M.
Author_Institution :
Machine Vision International, Ann Arbor, MI 48104.
Abstract :
Relational models are frequently used in high-level computer vision. Finding a correspondence between a relational model and an image description is an important operation in the analysis of scenes. In this paper the process of finding the correspondence is formalized by defining a general relational distance measure that computes a numeric distance between any two relational descriptions-a model and an image description, two models, or two image descriptions. The distance measure is proved to be a metric, and is illustrated with examples of distance between object models. A variant measure used in our past studies is shown not to be a metric.
Keywords :
Bayesian methods; Computer vision; Decision theory; Density functional theory; Image analysis; Layout; Machine vision; Pattern recognition; Prototypes; Testing; Matching; metric; relational distance; structural description;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
DOI :
10.1109/TPAMI.1985.4767621