Towards a general theory of action and time
Artificial Intelligence
Self-organizing maps
PicSOM—content-based image retrieval with self-organizing maps
Pattern Recognition Letters - Selected papers from the 11th scandinavian conference on image analysis
Unsupervised document classification using sequential information maximization
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Artificial Intelligence in Design '00
Artificial Intelligence in Design '00
Transformations in Design: A Formal Approach to Stylistic Change and Innovation in the Visual Arts
Transformations in Design: A Formal Approach to Stylistic Change and Innovation in the Visual Arts
Computers and Intractability; A Guide to the Theory of NP-Completeness
Computers and Intractability; A Guide to the Theory of NP-Completeness
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Qualitative Spatial Representation and Reasoning Techniques
KI '97 Proceedings of the 21st Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence
Target Testing and the PicHunter Bayesian Multimedia Retrieval System
ADL '96 Proceedings of the 3rd International Forum on Research and Technology Advances in Digital Libraries
Learning to Visualise High-Dimensional Data
IV '04 Proceedings of the Information Visualisation, Eighth International Conference
Visual analogy in problem solving
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Psychological challenges for the analysis of style
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Hi-index | 0.00 |
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.