Supervised genetic search for parameter selection in painterly rendering

  • Authors:
  • John P. Collomosse

  • Affiliations:
  • Department of Computer Science, University of Bath, Bath, UK

  • Venue:
  • EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
  • Year:
  • 2006

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Abstract

This paper investigates the feasibility of evolutionary search techniques as a mechanism for interactively exploring the design space of 2D painterly renderings. Although a growing body of painterly rendering literature exists, the large number of low-level configurable parameters that feature in contemporary algorithms can be counter-intuitive for non-expert users to set. In this paper we first describe a multi-resolution painting algorithm capable of transforming photographs into paintings at interactive speeds. We then present a supervised evolutionary search process in which the user scores paintings on their aesthetics to guide the specification of their desired painterly rendering. Using our system, non-expert users are able to produce their desired aesthetic in approximately 20 mouse clicks — around half an order of magnitude faster than manual specification of individual rendering parameters by trial and error.