Statistical regularities of art images and natural scenes: Spectra, sparseness and nonlinearities
Statistical regularities of art images and natural scenes: Spectra, sparseness and nonlinearities
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DOI：
10.1163/156856808782713771
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发表时间：
2008-01-01
期刊：
影响因子：
--
通讯作者：
Field, David J.
中科院分区：
文献类型：
--
作者：
Graham, Daniel J.;Field, David J.
绘画是一个过程的产物，这个过程始于自然世界中的普通视觉，并以画布上的颜料操作结束。由于艺术家必须产生的图像，可以看到的视觉系统，被认为是利用统计的自然场景中的，艺术家很可能会复制许多这些自然的艺术在他们的绘画艺术。我们已经测试了这个概念，通过计算基本的统计特性和建模细胞响应特性的一个大的数字化绘画和自然场景。我们发现，代表性和非代表性（抽象）绘画从我们的样本（124张图像）显示基本相似的自然场景的样本在其空间频率振幅谱，但绘画和自然场景显示出显着不同的平均振幅谱斜率。我们还发现，绘画的强度分布表现出较低的偏度和稀疏性比自然场景。我们通过考虑在环境中发现的亮度范围与在油漆介质中可用的范围相比来解释这一点。一幅画的范围受到其材料的反射特性的限制。我们认为，艺术家不简单地规模的强度范围，但使用压缩非线性。在我们的研究中，模拟的视网膜和皮质滤波器对图像的响应在绘画中比在自然场景中更稀疏。但是，当压缩非线性应用于图像时，与自然场景相比，绘画的稀疏性和对绘画的建模响应都显示出相同或更大的稀疏性。这表明艺术家在他们的绘画中实现了某种程度的非线性压缩。因为绘画已经吸引了人类几千年，在绘画的空间结构中找到基本的统计学特征可以让人们深入了解人类发现引人注目的空间模式。
Paintings are the product of a process that begins with ordinary vision in the natural world and ends with manipulation of pigments on canvas. Because artists must produce images that can be seen by a visual system that is thought to take advantage of statistical regularities in natural scenes, artists are likely to replicate many of these regularities in their painted art. We have tested this notion by computing basic statistical properties and modeled cell response properties for a large set of digitized paintings and natural scenes. We find that both representational and nonrepresentational (abstract) paintings from our sample (124 images) show basic similarities to a sample of natural scenes in terms of their spatial frequency amplitude spectra, but the paintings and natural scenes show significantly different mean amplitude spectrum slopes. We also find that the intensity distributions of paintings show a lower skewness and sparseness than natural scenes. We account for this by considering the range of luminances found in the environment compared to the range available in the medium of paint. A painting's range is limited by the reflective properties of its materials. We argue that artists do not simply scale the intensity range down but use a compressive nonlinearity. In our studies, modeled retinal and cortical filter responses to the images were less sparse for the paintings than for the natural scenes. But when a compressive nonlinearity was applied to the images, both the paintings' sparseness and the modeled responses to the paintings showed the same or greater sparseness compared to the natural scenes. This suggests that artists achieve some degree of nonlinear compression in their paintings. Because paintings have captivated humans for millennia, finding basic statistical regularities in paintings' spatial structure could grant insights into the range of spatial patterns that humans find compelling.