Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/128476
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Type: Conference paper
Title: On the use of colour-based segmentation in evolutionary image composition
Author: Neumann, A.
Neumann, F.
Citation: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2018), 2018 / pp.1-8
Publisher: IEEE
Publisher Place: Piscataway, NJ
Issue Date: 2018
Series/Report no.: IEEE Congress on Evolutionary Computation
ISBN: 9781509060177
Conference Name: IEEE Congress on Evolutionary Computation (CEC) (08 Jul 2018 - 13 Jul 2018 : Rio de Janeiro, Brazil)
Statement of
Responsibility: 
Aneta Neumann, Frank Neumann
Abstract: Evolutionary algorithms have been widely used in the area of creativity in order to help create art and music. We consider the recently introduced evolutionary image composition approach based on feature covariance matrices [1] which allows composing two images into a new one based on their feature characteristics. When using evolutionary image composition it is important to obtain a good weighting of interesting regions of the two images. We use colour-based segmentation based on K-Means clustering to come up with such a weighting of the images. Our results show that this preserves the chosen colour regions of the images and leads to composed images that preserve colours better than the previous approach based on saliency masks [1]. Furthermore, we evaluate our composed images in terms of aesthetic feature and show that our approach based on colour-based segmentation leads to higher feature values for most of the investigated features.
Description: Part of the IEEE World Congress on Computational Intelliegence (IEEE WCCI 2018). Which includes conferences IJCNN 2018, IEEE CEC 2018 and FUZZ-IEEE 2018.
Rights: ©2018 IEEE
RMID: 0030096491
DOI: 10.1109/CEC.2018.8477973
Published version: https://ieeexplore.ieee.org/xpl/conhome/8466244/proceeding
Appears in Collections:Computer Science publications

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