Soft Mixer Assignment in a Hierarchical Generative Model of Natural Scene Statistics
Soft Mixer Assignment in a Hierarchical Generative Model of Natural Scene Statistics
复制标题
自然场景统计的分层生成模型中的软混合器分配
作者：
O. Schwartz;T. Sejnowski;P. Dayan
高斯尺度混合模型提供了信号生成的自上而下的描述，捕获了滤波器对图像响应的关键自下而上的统计特征。然而，此类模型的过滤器之间的依赖模式是预先指定的。我们提出了高斯尺度混合模型的一种新颖扩展，该模型从观察到的输入中学习依赖模式，从而引入这些输入的层次表示。具体来说，我们建议输入由高斯变量（建模局部滤波器结构）生成，乘以混合器变量，该变量按概率分配给来自一组可能混合器的每个输入。我们针对合成数据和不同类别的自然图像（例如通用整体和人脸）演示了生成模型的两个组件的推理。对于自然图像，混合器变量分配显示出类似于视觉皮层中复杂细胞的不变性；模型高斯分量的统计数据与除法归一化模型的输出一致。我们还展示了我们的模型如何帮助将各种图像统计和皮质处理模型相互关联。
Gaussian scale mixture models offer a top-down description of signal generation that captures key bottom-up statistical characteristics of filter responses to images. However, the pattern of dependence among the filters for this class of models is prespecified. We propose a novel extension to the gaussian scale mixturemodel that learns the pattern of dependence from observed inputs and thereby induces a hierarchical representation of these inputs. Specifically, we propose that inputs are generated by gaussian variables (modeling local filter structure), multiplied by a mixer variable that is assigned probabilistically to each input from a set of possible mixers. We demonstrate inference of both components of the generative model, for synthesized data and for different classes of natural images, such as a generic ensemble and faces. For natural images, the mixer variable assignments show invariances resembling those of complex cells in visual cortex; the statistics of the gaussian components of the model are in accord with the outputs of divisive normalization models. We also show how our model helps interrelate a wide range of models of image statistics and cortical processing.
DOI：
10.1364/josaa.4.002379
发表时间：
1987-12-01
影响因子：
1.9
作者：
FIELD, DJ
通讯作者：
FIELD, DJ