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Visual style: Qualitative and context-dependent categorization

Published online by Cambridge University Press:  27 June 2006

JULIE JUPP  and
JOHN S. GERO
JULIE JUPP
Affiliation:
Engineering Design Centre, University of Cambridge, Cambridge, UK
JOHN S. GERO
Affiliation:
Key Centre of Design Computing and Cognition, School of Architecture, Design Science and Planning, University of Sydney, Sydney, New South Wales, Australia
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Abstract

Style is an ordering principle by which to structure artifacts in a design domain. The application of a visual order entails some explicit grouping property that is both cognitively plausible and contextually dependent. Central to cognitive–contextual notions are the type of representation used in analysis and the flexibility to allow semantic interpretation. We present a model of visual style based on the concept of similarity as a qualitative context-dependent categorization. The two core components of the model are semantic feature extraction and self-organizing maps (SOMs). The model proposes a method of categorizing two-dimensional unannotated design diagrams using both low-level geometric and high-level semantic features that are automatically derived from the pictorial content of the design. The operation of the initial model, called Q-SOM, is then extended to include relevance feedback (Q-SOM:RF). The extended model can be seen as a series of sequential processing stages, in which qualitative encoding and feature extraction are followed by iterative recategorization. Categorization is achieved using an unsupervised SOM, and contextual dependencies are integrated via cluster relevance determined by the observer's feedback. The following stages are presented: initial per feature detection and extraction, selection of feature sets corresponding to different spatial ontologies, unsupervised categorization of design diagrams based on appropriate feature subsets, and integration of design context via relevance feedback. From our experiments we compare different outcomes from consecutive stages of the model. The results show that the model provides a cognitively plausible and context-dependent method for characterizing visual style in design.

Keywords

Design ContextQualitative Spatial RepresentationRelevance FeedbackSimilarity

Information

Type
Research Article
Information
AI EDAM , Volume 20 , Issue 3 , August 2006 , pp. 247 - 266
Copyright
© 2006 Cambridge University Press

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