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The Power of Visual Texture in Aesthetic Perception: An Exploration of the Predictability of Perceived Aesthetic Emotions

机译:审美意识中视觉质感的力量:审美情感的可预测性探讨

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摘要

How to interpret the relationship between the low-level features, such as some statistical characteristics of color and texture, and the high-level aesthetic properties, such as warm or cold, soft or hard, has been a hot research topic of neuroaesthetics. Contrary to the black-box method widely used in the fields of machine learning and pattern recognition, we build a white-box model with the hierarchical feed-forward structure inspired by neurobiological mechanisms underlying the aesthetic perception of visual art. In the experiment, the aesthetic judgments for 8 pairs of aesthetic antonyms are carried out for a set of 151 visual textures. For each visual texture, 106 low-level features are extracted. Then, ten more useful and effective features are selected through neighborhood component analysis to reduce information redundancy and control the complexity of the model. Finally, model building of the beauty appreciation of visual textures using multiple linear or nonlinear regression methods is detailed. Compared with our previous work, a more robust feature selection algorithm, neighborhood component analysis, is used to reduce information redundancy and control computation complexity of the model. Some nonlinear models are also adopted and achieved higher prediction accuracy when compared with the previous linear models. Additionally, the selection strategy of aesthetic antonyms and the selection standards of the core set of them are also explained. This research also suggests that the aesthetic perception and appreciation of visual textures can be predictable based on the computed low-level features.
机译:如何解释诸如颜色和​​纹理的一些统计特征等低级特征与诸如冷热,软或硬之类的高级美学属性之间的关系,一直是神经美学的研究热点。与在机器学习和模式识别领域广泛使用的黑匣子方法相反,我们建立了一个白匣子模型,该模型具有受视觉艺术审美感知背后的神经生物学机制启发的分层前馈结构。在实验中,对一组151种视觉纹理进行了8对美学反义词的美学判断。对于每个视觉纹理,提取106个低级特征。然后,通过邻域分量分析选择十个更有用和有效的特征,以减少信息冗余并控制模型的复杂性。最后,详细介绍了使用多种线性或非线性回归方法建立视觉纹理美感的模型。与我们以前的工作相比,使用了更强大的特征选择算法邻域分量分析,以减少信息冗余并控制模型的计算复杂性。与以前的线性模型相比,还采用了一些非线性模型并获得了更高的预测精度。此外,还介绍了美学反义词的选择策略和核心反义词的选择标准。这项研究还表明,基于计算出的低级特征,视觉纹理的美学感知和欣赏是可以预测的。

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