title: Abstract art by shape classification.
abstract: 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.
authors.affiliation: School of Electronic Engineering and Computer Science Queen Mary, University of London, London. yzs@eecs.qmul.ac.uk
authors.forename: Yi-Zhe
authors.initials: YZ
authors.lastname: Song
authors.forename: David
authors.initials: D
authors.lastname: Pickup
authors.forename: Chuan
authors.initials: C
authors.lastname: Li
authors.forename: Paul
authors.initials: P
authors.lastname: Rosin
authors.forename: Peter
authors.initials: P
authors.lastname: Hall
mesh_terms: D000465:Algorithms; D001154:Art; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D010781:Photography