Skip to main navigation Skip to search Skip to main content

Probability issues in locality descriptions based on Voronoi neighbor relationship

  • Peking University
  • Harbin Institute of Technology Shenzhen
  • South China University of Technology

Research output: Contribution to journalArticlepeer-review

Abstract

Spatial relationships play an important role in spatial knowledge representation, such as in describing localities. However, little attention has been paid to how to describe the position of a target object (TO) with a qualitative referencing system that consists of a set of reference objects (ROs) in the locality description context. We propose a method that accounts for the differences between two scenarios in locality descriptions. This method is probabilistic and is based on the Voronoi neighbor relationship to determine candidate ROs for describing a given TO's position in the second scenario. The Voronoi neighbor relationship is adopted to determine candidate ROs of a TO and to compute the neighboring area of an RO. A probability function is presented to model the uncertainty of selecting appropriate ROs. To build locality descriptions that are consistent with commonsense, four constraints are placed on the probability function. Two probability functions based on Euclidean distance and stolen-area, and a mixed probability function that considers both Euclidean distance and stolen-area, are analyzed and compared. With the mixed probability function, we establish a method to construct the locality description of a given TO. Finally, three examples demonstrate how to select ROs to describe a TO's position.

Original languageEnglish
Pages (from-to)213-222
Number of pages10
JournalJournal of Visual Languages and Computing
Volume23
Issue number4
DOIs
StatePublished - Aug 2012
Externally publishedYes

Keywords

  • Locality description
  • Probability function
  • Voronoi diagram
  • Voronoi neighbor relationship

Fingerprint

Dive into the research topics of 'Probability issues in locality descriptions based on Voronoi neighbor relationship'. Together they form a unique fingerprint.

Cite this