Supervised Genetic Search for Parameter Selection in Painterly Rendering

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, nonexpert 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.

• Type: Conference
• Year: 2006
• Where: EVOW
• Authors: John P. Collomosse
• Keywords: Artificial Intelligence, Evolutionary Search, EVOW 2006, Low-level Configurable Parameters, Painterly