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Abstract art by shape classification

Song, Yi-Zhe, Pickup, D., Li, C., Rosin, P. and Hall, P. (2013) Abstract art by shape classification IEEE Transactions on Visualization and Computer Graphics, 19 (8). pp. 1252-1263.

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This paper shows that classifying shapes is a tool useful in nonphotorealistic rendering (NPR) from photographs. Our classifier inputs regions from an image segmentation hierarchy and outputs the 'best” fitting simple shape such as a circle, square, or triangle. Other approaches to NPR have recognized the benefits of segmentation, but none have classified the shape of segments. By doing so, we can create artwork of a more abstract nature, emulating the style of modern artists such as Matisse and other artists who favored shape simplification in their artwork. The classifier chooses the shape that 'best” represents the region. Since the classifier is trained by a user, the 'best shape” has a subjective quality that can over-ride measurements such as minimum error and more importantly captures user preferences. Once trained, the system is fully automatic, although simple user interaction is also possible to allow for differences in individual tastes. A gallery of results shows how this classifier contributes to NPR from images by producing abstract artwork.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Pickup, D.
Li, C.
Rosin, P.
Hall, P.
Date : 14 February 2013
DOI : 10.1109/TVCG.2013.13
Uncontrolled Keywords : Abstract art; Nonphotorealistic rendering; Shape classification; Shape fitting
Additional Information : No further action
Depositing User : Clive Harris
Date Deposited : 30 Jul 2019 09:02
Last Modified : 30 Jul 2019 09:02

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