Abstract
The concepts of order and complexity and their quantitative evaluation have been at the core of computational notion of aesthetics. One of the major challenges is conforming human intuitive perception and what we perceive as aesthetically pleasing with the output of a computational model. Informational theories of aesthetics have taken advantage of entropy in measuring order and complexity of stimuli in relation to their aesthetic value. However entropy fails to discriminate structurally different patterns in a 2D plane. In this work, following an overview on symmetry and its significance in the domain of aesthetics, a nature-inspired, swarm intelligence technique (Dispersive Flies Optimisation or DFO) is introduced and then adapted to detect symmetries and quantify symmetrical complexities in images. The 252 Jacobsen & Höfel’s images used in this paper are created by researchers in the psychology and visual domain as part of an experimental study on human aesthetic perception. Some of the images are symmetrical and some are asymmetrical, all varying in terms of their aesthetics, which are ranked by humans. The results of the presented nature-inspired algorithm is then compared to what humans in the study aesthetically appreciated and ranked. Whilst the authors believe there is still a long way to have a strong correlation between a computational model of complexity and human appreciation, the results of the comparison are promising.
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Notes
- 1.
Despite the algorithm’s simplicity, it is shown that DFO outperforms the standard versions of the well-known Particle Swarm Optimisation, Genetic Algorithm (GA) as well as Differential Evolution (DE) algorithms on an extended set of benchmarks over three performance measures of error, efficiency and reliability [1]. It is shown that DFO is more efficient in 84.62% and more reliable in 90% of the 28 standard optimisation benchmarks used; furthermore, when there exists a statistically significant difference, DFO converges to better solutions in 71.05% of problem set.
- 2.
- 3.
The source code of DFO algorithm can be downloaded from the following web page: http://doc.gold.ac.uk/mohammad/DFO/.
- 4.
In this research, for simplicity, the difference of the sums of the two areas are considered. Other more sensitive or computationally expensive measures, such as the histogram of oriented gradients (HOG), could alternatively be used.
- 5.
In other words, the (x, y) coordinate of the best fly in each iteration is logged, giving rise to 300 coordinates (the number of iterations allowed) for evaluating each image.
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Acknowledgement
We are grateful to Thomas Jacobsen of Helmut Schmidt University for granting permission to use his experimental stimuli.
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al-Rifaie, M.M., Ursyn, A., Zimmer, R., Javid, M.A.J. (2017). On Symmetry, Aesthetics and Quantifying Symmetrical Complexity. In: Correia, J., Ciesielski, V., Liapis, A. (eds) Computational Intelligence in Music, Sound, Art and Design. EvoMUSART 2017. Lecture Notes in Computer Science(), vol 10198. Springer, Cham. https://doi.org/10.1007/978-3-319-55750-2_2
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