Linking KANSAI and Image Features by Multi-layer Neural Networks
KANSEI is a Japanese term which means psychological feeling or image of a product. KANSEI engineering refers to the translation of consumers’ psychological feeling about a product into perceptual design elements. Recently several researches have been done for image indexing or image retrieval based on KANSEI factors. In this paper, we report a quantitative study on relationship between image color features and human KNASEI factors. We use the semantic differential (SD) method to extract the KANSEI factors (impressions) such as bright, warm from 4 group subjects (Children, students, adults, elderly person) while they viewing an image (painting). A neural network is used to learn the mapping functions (relationships) from the image feature space to human KANSEI factor space (psychological space).
KeywordsKANSEI impression image color neural network SD method
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