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, 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.

[1]  John P. Collomosse,et al.  Genetic Paint: A Search for Salient Paintings , 2005, EvoWorkshops.

[2]  Alexander Kolliopoulos,et al.  Image Segmentation for Stylized Non-Photorealistic Rendering and Animation , 2005 .

[3]  Douglas DeCarlo,et al.  Abstracted painterly renderings using eye-tracking data , 2002, NPAR '02.

[4]  Erol Gelenbe,et al.  Learning in Genetic Algorithms , 1998, ICES.

[5]  David Salesin,et al.  Computer-generated watercolor , 1997, SIGGRAPH.

[6]  Francisco Herrera,et al.  Learning with Genetic Algorithms , 2001 .

[7]  Ken Perlin,et al.  Painterly rendering for video and interaction , 2000, NPAR '00.

[8]  Tore Kristensen,et al.  The Meaning of Colour , 2003 .

[9]  Marc Ebner,et al.  Evolution of Vertex and Pixel Shaders , 2005, EuroGP.

[10]  Scott Draves,et al.  The Electric Sheep Screen-Saver: A Case Study in Aesthetic Evolution , 2005, EvoWorkshops.

[11]  Irfan A. Essa,et al.  Image and video based painterly animation , 2004, NPAR '04.

[12]  Michio Shiraishi,et al.  An algorithm for automatic painterly rendering based on local source image approximation , 2000, NPAR '00.

[13]  Rolf Drechsler,et al.  Applications of Evolutionary Computing, EvoWorkshops 2008: EvoCOMNET, EvoFIN, EvoHOT, EvoIASP, EvoMUSART, EvoNUM, EvoSTOC, and EvoTransLog, Naples, Italy, March 26-28, 2008. Proceedings , 2008, EvoWorkshops.

[14]  Peter Litwinowicz,et al.  Processing images and video for an impressionist effect , 1997, SIGGRAPH.

[15]  Kenneth DeJong,et al.  Learning with genetic algorithms: An overview , 1988, Machine Learning.

[16]  Peter Meer,et al.  Synergism in low level vision , 2002, Object recognition supported by user interaction for service robots.

[17]  Aaron Hertzmann,et al.  Painterly rendering with curved brush strokes of multiple sizes , 1998, SIGGRAPH.

[18]  Peter Shirley,et al.  Artistic Vision: painterly rendering using computer vision techniques , 2002, NPAR '02.

[19]  J. Russell.  The psychology of facial expression: Reading emotions from and into faces: Resurrecting a dimensional-contextual perspective , 1997 .

[20]  Karl Sims,et al.  Artificial evolution for computer graphics , 1991, SIGGRAPH.

[21]  J. Russell,et al.  The psychology of facial expression: Frontmatter , 1997 .

[22]  Douglas DeCarlo,et al.  Visual interest and NPR: an evaluation and manifesto , 2004, NPAR '04.

[23]  Paul Haeberli,et al.  Paint by numbers: abstract image representations , 1990, SIGGRAPH.

[24]  Margrit Betke,et al.  Empathic painting: interactive stylization through observed emotional state , 2006, NPAR.

[25]  F. Mahnke.  Color, Environmental and Human Response , 1996 .

[26]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[27]  Aaron Hertzmann,et al.  Paint by relaxation , 2001, Proceedings. Computer Graphics International 2001.

[28]  L. Rainwater,et al.  The meanings of color. , 1962, The Journal of general psychology.