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Photorealistic Rendering with an Ant Algorithm

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Computational Intelligence (IJCCI 2012)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 577))

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Abstract

Swarm art is a subfield of a contemporary digital art trend, called generative art, which uses swarm intelligence for creative purposes. Swarm intelligence is a computational paradigm that relies on a population of simple entities that interact with each other and/or with the environment by means of simple rules. KANTS consists of a population of input data vectors that communicate through a 2-dimensional heterogeneous grid of vectors, updating those vectors in the process. The algorithm was originally designed for data analysis. This paper describes recent swarm art experiments with an ant-based clustering algorithm called KANTS, which is noved here from its scientific framework and used as a stochastic generator of coloured paintings. Different types of data have been used as input vectors and two different approaches for initializing the grid of vectors were tested, each one leading to different types of images: figurative and abstract. The paper gives an overview on the application of KANTS to swarm art, and describes the most recent experiments with the algorithm in photo rendering. Data from the red, green and blue (RGB) color representation data of photographs are used as input and grid vectors in order to generate realistic versions of those same photos. We call the images pherogenic paintings, since the output vectors used for generating them are the pheromone maps of the ant algorithm. As a creative tool, the method is contextualized within the swarm art field.

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Correspondence to Carlos M. Fernandes .

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Fernandes, C.M., Mora, A.M., Merelo, J.J., Rosa, A.C. (2015). Photorealistic Rendering with an Ant Algorithm. In: Madani, K., Correia, A., Rosa, A., Filipe, J. (eds) Computational Intelligence. IJCCI 2012. Studies in Computational Intelligence, vol 577. Springer, Cham. https://doi.org/10.1007/978-3-319-11271-8_5

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  • DOI: https://doi.org/10.1007/978-3-319-11271-8_5

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11270-1

  • Online ISBN: 978-3-319-11271-8

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