---
title: "Fuzzy Conceptual Graphs for Matching Images of Natural Scenes"
authors:
  - "Philippe Mulhem"
  - "Wee Kheng Leow"
  - "Yoong Keok Lee"
doi: ""
url: "https://ijcai.org/proceedings/2001/184"
pdf: "https://ijcai.org/proceedings/2001/0184.pdf"
venue: "Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence"
year: 2001
pages: "1397-1403"
track: ""
keywords:
---

# Fuzzy Conceptual Graphs for Matching Images of Natural Scenes

Philippe Mulhem, Wee Kheng Leow, Yoong Keok Lee

*Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence*, pages 1397-1403.

## Abstract

Conceptual graphs are very useful for representing structured knowledge. However, existing formulations of fuzzy conceptual graphs are not suitable for matching images of natural scenes. This paper presents a new variation of fuzzy conceptual graphs that is more suited to image matching. This variant differentiates between a model graph that describes a known scene and an image graph which describes an input image. A new measurement is defined to measure how well a model graph matches an image graph. A fuzzy graph matching algorithm is developed based on error-tolerant subgraph isomorphism. Test results show that the matching algorithm gives very good results for matching images to predefined scene models