Optimisation and Logistics, School of Computer Science, The University of Adelaide, Australia
Evol Comput. 2020 Winter;28(4):643-675. doi: 10.1162/evco_a_00270. Epub 2020 Feb 26.
We present a study demonstrating how random walk algorithms can be used for evolutionary image transition. We design different mutation operators based on uniform and biased random walks and study how their combination with a baseline mutation operator can lead to interesting image transition processes in terms of visual effects and artistic features. Using feature-based analysis we investigate the evolutionary image transition behaviour with respect to different features and evaluate the images constructed during the image transition process. Afterwards, we investigate how modifications of our biased random walk approaches can be used for evolutionary image painting. We introduce an evolutionary image painting approach whose underlying biased random walk can be controlled by a parameter influencing the bias of the random walk and thereby creating different artistic painting effects.
我们提出了一项研究，展示了如何使用随机游走算法进行进化图像转换。我们设计了基于均匀和有偏随机游走的不同突变算子，并研究了它们与基线突变算子的组合如何在视觉效果和艺术特征方面导致有趣的图像转换过程。使用基于特征的分析，我们研究了不同特征下的进化图像转换行为，并评估了图像转换过程中构建的图像。然后，我们研究了如何修改我们的有偏随机游走方法来进行进化图像绘画。我们引入了一种进化图像绘画方法，其底层的有偏随机游走可以通过一个参数来控制，该参数影响随机游走的偏差，从而产生不同的艺术绘画效果。
