Name
Abstract | ||
|---|---|---|
In this paper, we apply the algorithms to facilitate learning to kansei modeling and experimentally investigate constructed kansei model itself. We introduce using a vector space as a scheme of the mental representation and place still images in the perceptual space by generating perceptual features. Furthermore we propose a method to manipulate the perceptual data by optimizing modeling parameters based on the kansei scale. After this adaptation we compare the similarity between the kansei clusters using their distance in the space to evaluate if the adapting perceptual space is appropriate for one's kansei. We have conducted preliminary experiments utilizing image data of TV commercials and briefly evaluated the mental space constructed by our method through the kansei questionnaire. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1007/3-540-45656-2_31 | Australian Joint Conference on Artificial Intelligence |
Keywords | Field | DocType |
adaptive perceptual space,perceptual feature,adapting perceptual space,kansei cluster,kansei model,vector space,mental space,kansei scale,perceptual data,kansei patterns,kansei questionnaire,perceptual space,mental representation | Similitude,Vector space,Color space,Pattern recognition,Adaptive method,Computer science,Kansei,Artificial intelligence,Perception,Machine learning,Mental representation | Conference |
ISBN | Citations | PageRank |
3-540-42960-3 | 0 | 0.34 |
References | Authors | |
5 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| tomoko murakami | 1 | 63 | 5.48 |
| ryohei orihara | 2 | 86 | 15.77 |
| Naomichi Sueda | 3 | 4 | 2.45 |