Bayesian hierarchical grouping: Perceptual grouping as mixture estimation

Psychological Review
Vicky FroyenManish Singh

Abstract

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 Heller and Ghahramani (2005). 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...Continue Reading

Citations

Jun 30, 2016·Vision Research·Frank JäkelMichael H Herzog
Sep 16, 2016·Cognitive Psychology·Filipp Schmidt, Roland W Fleming
Sep 28, 2016·Perception·Luigi Burigana, Michele Vicovaro
Jul 13, 2017·Perception·Peter A van der Helm
Jan 13, 2021·Nature Communications·Gábor LengyelJózsef Fiser
Oct 30, 2021·PLoS Computational Biology·Jennifer Laura Lee, Wei Ji Ma

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