Karklin Yan, Lewicki Michael S
Computer Science Department and Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Network. 2003 Aug;14(3):483-99.
The theoretical principles that underlie the representation and computation of higher-order structure in natural images are poorly understood. Recently, there has been considerable interest in using information theoretic techniques, such as independent component analysis, to derive representations for natural images that are optimal in the sense of coding efficiency. Although these approaches have been successful in explaining properties of neural representations in the early visual pathway and visual cortex, because they are based on a linear model, the types of image structure that can be represented are very limited. Here, we present a hierarchical probabilistic model for learning higher-order statistical regularities in natural images. This non-linear model learns an efficient code that describes variations in the underlying probabilistic density. When applied to natural images the algorithm yields coarse-coded, sparse-distributed representations of abstract image properties such as object location, scale and texture. This model offers a novel description of higher-order image structure and could provide theoretical insight into the response properties and computational functions of lower level cortical visual areas.
自然图像中高阶结构的表示和计算所依据的理论原理目前还知之甚少。最近，人们对使用信息论技术（如独立成分分析）来推导自然图像的表示形式产生了浓厚兴趣，这些表示形式在编码效率方面是最优的。尽管这些方法在解释早期视觉通路和视觉皮层中神经表示的特性方面取得了成功，但由于它们基于线性模型，所能表示的图像结构类型非常有限。在此，我们提出一种用于学习自然图像中高阶统计规律的分层概率模型。这个非线性模型学习一种有效的编码，用于描述潜在概率密度的变化。当应用于自然图像时，该算法会生成诸如物体位置、尺度和纹理等抽象图像属性的粗编码、稀疏分布表示。此模型为高阶图像结构提供了一种新颖的描述，并可能为低级皮层视觉区域的响应特性和计算功能提供理论见解。
