Virtual Photography Using Multi-Objective Particle Swarm Optimization

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Reference

W. Barry and Brian J. Ross: Virtual Photography Using Multi-Objective Particle Swarm Optimization. GECCO 2014, pp. 285-292, Vancouver, BC, July 2014.

DOI

http://dx.doi.org/10.1145/2576768.2598210

Abstract

Particle swarm optimization (PSO) is a stochastic population-based search algorithm that is inspired by the flocking behaviour of birds. Here, a PSO is used to implement swarms of cameras flying through a virtual world in search of an image that satisfies a set of compositional objectives, for example, the rule of thirds and horizon line rules. To effectively process these multiple, and possible conflicting, criteria, a new multi-objective PSO algorithm called the sum of ranks PSO (SR-PSO) is introduced. The SR-PSO is useful for solving high-dimensional search problems, while discouraging degenerate solutions that can arise with other approaches. Less user intervention is required for the SR-PSO, as compared to a conventional PSO. A number of problems using different virtual worlds and user-supplied objectives were studied. In all cases, solution images were obtained that satisfied the majority of given objectives. The SR-PSO is shown to be superior to other algorithms in solving the high-dimensional virtual photography problems studied.

Extended Abstract

Bibtex

Used References

Rafid Abdullah , Marc Christie , Guy Schofield , Christophe Lino , Patrick Olivier, Advanced composition in virtual camera control, Proceedings of the 11th international conference on Smart graphics, July 18-20, 2011, Bremen, Germany http://dl.acm.org/citation.cfm?id=2032570&CFID=588525319&CFTOKEN=29804931

Autodesk. 3ds max 2009. http://usa.autodesk.com/, August 2012.

William Bares , Byungwoo Kim, Generating virtual camera compositions, Proceedings of the 6th international conference on Intelligent user interfaces, p.9-12, January 14-17, 2001, Santa Fe, New Mexico, USA http://doi.acm.org/10.1145/359784.359824

W. Barry. Generating aesthetically pleasing images in a virtual environment using particle swarm optimization. Master's thesis, Brock U., 2012.

P. Bentley and J. Wakefield. Finding acceptable solutions in the pareto-optimal range using multiobjective genetic algorithms. In P. C. et al., editor, Soft Computing in Engineering Design and Manufacturing, pages 231--240. Springer-Verlag, 1998.

S. Bergen and B. Ross. Evolutionary Art Using Summed Multi-objective Ranks. In Genetic Programming - Theory and Practice VIII, pages 227--244. Springer, May 2010.

Subhabrata Bhattacharya , Rahul Sukthankar , Mubarak Shah, A holistic approach to aesthetic enhancement of photographs, ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), v.7S n.1, October 2011 http://doi.acm.org/10.1145/2037676.2037678

Paolo Burelli , Luca Gaspero , Andrea Ermetici , Roberto Ranon, Virtual Camera Composition with Particle Swarm Optimization, Proceedings of the 9th international symposium on Smart Graphics, August 27-29, 2008, Rennes, France http://dx.doi.org/10.1007/978-3-540-85412-8_12

P. Burelli and M. Preuss. Automatic camera control: a dynamic multi-objective perspective. In Evostar 2014. Springer, April 2014.

Peter K. Burian, Mastering Digital Photography and Imaging, SYBEX Inc., Alameda, CA, 2004 http://dl.acm.org/citation.cfm?id=984308&CFID=588525319&CFTOKEN=29804931

Carlos A. Coello Coello , Gary B. Lamont , David A. Van Veldhuizen, Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation), Springer-Verlag New York, Inc., Secaucus, NJ, 2006 http://dl.acm.org/citation.cfm?id=1215640&CFID=588525319&CFTOKEN=29804931

David W. Corne , Joshua D. Knowles, Techniques for highly multiobjective optimisation: some nondominated points are better than others, Proceedings of the 9th annual conference on Genetic and evolutionary computation, July 07-11, 2007, London, England http://doi.acm.org/10.1145/1276958.1277115

Kalyanmoy Deb , Deb Kalyanmoy, Multi-Objective Optimization Using Evolutionary Algorithms, John Wiley & Sons, Inc., New York, NY, 2001 http://dl.acm.org/citation.cfm?id=559152&CFID=588525319&CFTOKEN=29804931

S. Desroche, V. Jolivet, and D. Plemenos. Towards plan-based automatic exploration of virtual worlds. Proc. Int'l Conf. Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG)., 1:25--32, 2007.

R. Eberhart and J. Kennedy. A new optimizer using particle swarm theory. Proc. IEEE Intl. Conf. Neural Networks, 4:1942--1948, 1995.

J. Fieldsend. Multi-objective particle swarm optimisation methods. Technical report, TR419, Dept. Computer Science, U. Exeter, 2004.

B. Fier. Composition Photo Workshop. Wiley, 2007.

Michael Freeman, The Photographer's Eye: Composition and Design for Better Digital Photos, Focal Press, 2007 http://dl.acm.org/citation.cfm?id=1535309&CFID=588525319&CFTOKEN=29804931

Luca Gaspero , Andrea Ermetici , Roberto Ranon, Swarming in a Virtual World: A PSO Approach to Virtual Camera Composition, Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence, September 22-24, 2008, Brussels, Belgium http://dx.doi.org/10.1007/978-3-540-87527-7_14

Nathan Michael , Jonathan Fink , Vijay Kumar, Cooperative manipulation and transportation with aerial robots, Autonomous Robots, v.30 n.1, p.73-86, January 2011 http://dx.doi.org/10.1007/s10514-010-9205-0

S. Mostaghim and J. Teich. Strategies for finding good local guides in multi-objective particle swarm optimization (mopso). In Proc. IEEE Swarm Intelligence Symp., 2003.

Mike Preuss , Paolo Burelli , Georgios N. Yannakakis, Diversified virtual camera composition, Proceedings of the 2012t European conference on Applications of Evolutionary Computation, April 11-13, 2012, Málaga, Spain http://dx.doi.org/10.1007/978-3-642-29178-4_27

John R. Smith , Shih-Fu Chang, VisualSEEk: a fully automated content-based image query system, Proceedings of the fourth ACM international conference on Multimedia, p.87-98, November 18-22, 1996, Boston, Massachusetts, USA http://doi.acm.org/10.1145/244130.244151

R. Swanson, D. Escoffery, and A. Jhala. Learning visual composition preferences from an annotated corpus generated through gameplay. In Computational Intelligence and Games, pages 363--370. IEEE, Sept 2012.

F.-L. Zhang, M. Wang, and S.-M. Hu. Aesthetic Image Enhancement by Dependence-Aware Object Re-Composition. IEEE Trans. on Multimedia, 15, June 2013.


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Full Text

http://www.cosc.brocku.ca/~bross/psoPhotos/GECCO_2014_07_12.pptx Presentation

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