Name
Abstract | ||
|---|---|---|
An aesthetic learning model is proposed that applies evolutionary algorithm to generate art. The model is evaluated using an evolutionary art system by human subjects. The advantages of the model is that it helps user to automate the process of image evolution by learning the user's preferences and applying the knowledge to evolve aesthetical images. This paper implements four categories of aesthetic metrics to establish user's criteria. In addition to evolutionary images, external artworks are also included to guide evolutionary process towards more interesting paths. Then we described an evolutionary art system which adopted the aesthetic model in detail. Last, we evaluate the aesthetic learning model in several independent experiments to show the efficiency at predicting user's preferences. |
Year | DOI | Venue |
|---|---|---|
2011 | null | ECTA 2011/FCTA 2011: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON EVOLUTIONARY COMPUTATION THEORY AND APPLICATIONS AND INTERNATIONAL CONFERENCE ON FUZZY COMPUTATION THEORY AND APPLICATIONS |
Keywords | Field | DocType |
Aesthetic learning,Evolutionary art,Interactive evolutionary computation,Computational aesthetics | Engineering drawing,Art methodology,Computer science,Artificial intelligence,Machine learning | Conference |
Volume | Issue | Citations |
null | null | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yang Li | 1 | 22 | 2.86 |
| Changjun Hu | 2 | 130 | 27.56 |
| Jing-Qin Pang | 3 | 1 | 0.72 |