Fuzzy Conceptual Graphs for Matching Images of Natural Scenes
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
@inproceedings{ijcai2001p184,
title = {Fuzzy Conceptual Graphs for Matching Images of Natural Scenes},
author = {Mulhem, Philippe and Leow, Wee Kheng and Lee, Yoong Keok},
booktitle = {Proceedings of the Seventeenth International Joint Conference on
Artificial Intelligence, {IJCAI-01}},
publisher = {International Joint Conferences on Artificial Intelligence},
pages = {1397--1403},
year = {2001},
url = {https://ijcai.org/proceedings/2001/184},
}
TY - CPAPER TI - Fuzzy Conceptual Graphs for Matching Images of Natural Scenes AU - Mulhem, Philippe AU - Leow, Wee Kheng AU - Lee, Yoong Keok T2 - Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, IJCAI-01 PB - International Joint Conferences on Artificial Intelligence PY - 2001 SP - 1397 EP - 1403 UR - https://ijcai.org/proceedings/2001/184 ER -Copied
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