Learning aesthetic judgements in evolutionary art systems
Learning aesthetic judgements in evolutionary art systems
复制标题
学习进化艺术系统中的审美判断
DOI：
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发表时间：
2013
影响因子：
2.6
通讯作者：
Haolei Zuo
中科院分区：
文献类型：
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作者：
Yang Li;Changjun Hu;Leandro L. Minku;Haolei Zuo
在进化的艺术系统中，学习审美判断对于减少用户的疲劳至关重要。虽然判断美是一项高度主观的任务，但我们认为某些功能对于取悦用户很重要。在本文中，我们引入了一个自适应模型来学习互动进化艺术的任务中的审美判断。根据以前的工作，我们探索了一系列基于美学原则的美学测量。然后，我们通过特征选择将它们减少到一个相关的子集，并通过学习从以前的交互中提取的特征来构建模型。为了应用更精确的模型，比较了多层感知器和C4.5决策树分类器。为了检验该方法的有效性，采用该模型构建了一个进化的艺术系统，该系统分析了用户的审美判断，并在后代中近似了他们隐含的审美意图。我们首先在我们选定的艺术家的不同作品上测试这些美学测量。然后，一组用户进行了一系列的实验，以验证自适应学习模型。研究表明，不同的特征对识别不同的模式是有用的，但并不是所有的特征都与艺术家的风格描述相关。我们的研究结果表明，在进化艺术系统中使用的学习模型是健全的，并有前途的预测用户的喜好。
Learning aesthetic judgements is essential for reducing users’ fatigue in evolutionary art systems. Although judging beauty is a highly subjective task, we consider that certain features are important to please users. In this paper, we introduce an adaptive model to learn aesthetic judgements in the task of interactive evolutionary art. Following previous work, we explore a collection of aesthetic measurements based on aesthetic principles. We then reduce them to a relevant subset by feature selection, and build the model by learning the features extracted from previous interactions. To apply a more accurate model, multi-layer perceptron and C4.5 decision tree classifiers are compared. In order to test the efficacy of the approach, an evolutionary art system is built by adopting this model, which analyzes the user’s aesthetic judgements and approximates their implicit aesthetic intentions in the subsequent generations. We first tested these aesthetic measurements on different artworks from our selected artists. Then, a series of experiments were performed by a group of users to validate the adaptive learning model. The study reveals that different features are useful for identifying different patterns, but not all are relevant for the description of artists’ styles. Our results show that the use of the learning model in evolutionary art systems is sound and promising for predicting users’ preferences.