DocumentCode
1122431
Title
A Metric for Comparing Relational Descriptions
Author
Shapiro, Linda G. ; Haralick, Robert M.
Author_Institution
Machine Vision International, Ann Arbor, MI 48104.
Issue
1
fYear
1985
Firstpage
90
Lastpage
94
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;
fLanguage
English
Journal_Title
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher
ieee
ISSN
0162-8828
Type
jour
DOI
10.1109/TPAMI.1985.4767621
Filename
4767621
Link To Document