Bayesian hierarchical grouping: Perceptual grouping as mixture estimation.
Bayesian hierarchical grouping: Perceptual grouping as mixture estimation.
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DOI：
10.1037/a0039540
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发表时间：
2015-10
影响因子：
5.4
通讯作者：
Singh M
中科院分区：
文献类型：
--
作者：
Froyen V;Feldman J;Singh M
我们提出了一种基于混合模型思想的感知分组新框架，称为贝叶斯分层分组（BHG）。在BHG中，我们假设图像元素的配置是由不同对象的混合生成的，每个对象根据一些生成假设生成图像元素。在这个框架中，分组意味着估计生成图像的混合组件的数量和参数，包括估计哪些图像元素由哪些对象“拥有”。我们提出了一个易于处理的框架实现，基于的层次聚类方法。我们用一些经典的感知分组问题的例子来说明它，包括点聚类、轮廓积分和部分分解。我们的方法产生了图像元素的直观层次表示，将图像显式分解为混合组件，并估计了各种候选分解的概率。我们表明BHG很好地解释了从文献中得出的各种经验数据。因为BHG在广泛的分组问题上提供了分组解释的可行性的原则性量化，我们认为它提供了一个难以捉摸的格式塔Prägnanz概念的吸引人的统一说明。
We propose a novel framework for perceptual grouping based on the idea of mixture models, called Bayesian Hierarchical Grouping (BHG). In BHG we assume that the configuration of image elements is generated by a mixture of distinct objects, each of which generates image elements according to some generative assumptions. Grouping, in this framework, means estimating the number and the parameters of the mixture components that generated the image, including estimating which image elements are “owned” by which objects. We present a tractable implementation of the framework, based on the hierarchical clustering approach of. We illustrate it with examples drawn from a number of classical perceptual grouping problems, including dot clustering, contour integration, and part decomposition. Our approach yields an intuitive hierarchical representation of image elements, giving an explicit decomposition of the image into mixture components, along with estimates of the probability of various candidate decompositions. We show that BHG accounts well for a diverse range of empirical data drawn from the literature. Because BHG provides a principled quantification of the plausibility of grouping interpretations over a wide range of grouping problems, we argue that it provides an appealing unifying account of the elusive Gestalt notion of Prägnanz.