Conceptual structures: information processing in mind and machine
Conceptual structures: information processing in mind and machine
A New Algorithm for Error-Tolerant Subgraph Isomorphism Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
RELIEF: combining expressiveness and rapidity into a single system
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Knowledge representation: logical, philosophical and computational foundations
Knowledge representation: logical, philosophical and computational foundations
Similarity Searching in Medical Image Databases
IEEE Transactions on Knowledge and Data Engineering
ICCS '93 Proceedings on Conceptual Graphs for Knowledge Representation
ICCS '93 Proceedings on Conceptual Graphs for Knowledge Representation
Towards Fuzzy Conceptual Graph Programs
ICCS '96 Proceedings of the 4th International Conference on Conceptual Structures: Knowledge Representation as Interlingua
EMIR2: An Extended Model for Image Representation and Retrieval
DEXA '95 Proceedings of the 6th International Conference on Database and Expert Systems Applications
Matching utterances to rich knowledge structures to acquire a model of the speaker's goal
Proceedings of the 3rd international conference on Knowledge capture
Semantic image classification with hierarchical feature subset selection
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Fuzzy spatial relation ontology for image interpretation
Fuzzy Sets and Systems
Measuring the similarity of labeled graphs
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Incomplete and fuzzy conceptual graphs to automatically index medical reports
NLDB'07 Proceedings of the 12th international conference on Applications of Natural Language to Information Systems
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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.