Visual Element Property Graphs For Bridging The Symbol Description-Recognition Gap

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dc.contributor.advisorAijun An
dc.contributor.authorDehnen, Nicholas Alexander
dc.date.accessioned2025-07-23T15:17:21Z
dc.date.available2025-07-23T15:17:21Z
dc.date.copyright2025-04-15
dc.date.issued2025-07-23
dc.date.updated2025-07-23T15:17:20Z
dc.degree.disciplineComputer Science
dc.degree.levelMaster's
dc.degree.nameMSc - Master of Science
dc.description.abstractThis thesis addresses the semantic gap between visual perception and functional significance of symbols used in road vehicles. It presents a novel approach that enables users to identify and understand automotive symbols by describing what they visually perceive, translating visual descriptions into practical implications. A system combining a property graph representation of visual components and semantic relationships with a language model-powered natural language interface is developed. This method explicitly models relationships between visual elements and interpretations, differing from end-to-end vision-language models. Evaluations, using automated metrics and human assessment, demonstrate performance exceeding baseline large language models, with a BERTscore F1 of 0.765, compared to the best baseline's 0.597. Analysis of visual symbol queries reveals human description tendencies, favoring intuitive analogies and basic shapes. Contributions include a symbol decomposition methodology, an advanced property graph schema, natural language query processing, and evidence supporting structured knowledge representation for symbol description-recognition, applicable beyond automotive interfaces.
dc.identifier.urihttps://hdl.handle.net/10315/43017
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectComputer science
dc.subject.keywordsAutomotive symbols
dc.subject.keywordsSymbol recognition
dc.subject.keywordsSemantic gap
dc.subject.keywordsProperty graphs
dc.subject.keywordsKnowledge representation
dc.subject.keywordsLanguage models
dc.subject.keywordsNatural language interfaces
dc.subject.keywordsVisual description
dc.subject.keywordsSymbol decomposition
dc.subject.keywordsSemantic interpretation
dc.titleVisual Element Property Graphs For Bridging The Symbol Description-Recognition Gap
dc.typeElectronic Thesis or Dissertation

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