Spatial pattern discovery by learning a probabilistic parametric model from multiple attributed relational graphs

Pengyu Hong, Thomas S. Huang

Research output: Contribution to journalConference articlepeer-review

Abstract

This paper presents the methodology and theory for automatic spatial pattern discovery from multiple attributed relational graph samples. The spatial pattern is modelled as a mixture of probabilistic parametric attributed relational graphs. A statistic learning procedure is designed to learn the parameters of the spatial pattern model from the attributed relational graph samples. The learning procedure is formulated as a combinatorial non-deterministic process, which uses the expectation-maximization (EM) algorithm to find the maximum-likelihood estimates for the parameters of the spatial pattern model. The learned model summarizes the samples and captures the statistic characteristics of the appearance and structure of the spatial pattern, which is observed under various conditions. It can be used to detect the spatial pattern in new samples. The proposed approach is applied to unsupervised visual pattern extraction from multiple images in the experiments.

Original languageEnglish (US)
Pages (from-to)113-135
Number of pages23
JournalDiscrete Applied Mathematics
Volume139
Issue number1-3
DOIs
StatePublished - Apr 30 2004
Event2001 International Workshop on Combinatorial Image Analysis (IWCIA 2001) - Philadelphia, PA, United States
Duration: Aug 23 2001Aug 24 2001

Keywords

  • Attributed relational graph
  • EM algorithm
  • Parametric attributed relational graph
  • Spatial pattern discovery

ASJC Scopus subject areas

  • Discrete Mathematics and Combinatorics
  • Applied Mathematics

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