Virtual Photography Using Multi-Objective Particle Swarm Optimization
Inhaltsverzeichnis
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
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Links
Full Text
http://www.cosc.brocku.ca/~bross/psoPhotos/GECCO_2014_07_12.pptx Presentation
