TY - JOUR

T1 - Bayesian hierarchical grouping

T2 - Perceptual grouping as mixture estimation

AU - Froyen, Vicky

AU - Feldman, Jacob

AU - Singh, Manish

N1 - Publisher Copyright:
© 2015 American Psychological Association.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.

PY - 2015/10/1

Y1 - 2015/10/1

N2 - 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 a wide range of grouping problems, we argue that it provides an appealing unifying account of the elusive Gestalt notion of Prägnanz.

AB - 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 a wide range of grouping problems, we argue that it provides an appealing unifying account of the elusive Gestalt notion of Prägnanz.

KW - Bayesian inference

KW - Computational model

KW - Hierarchical representation

KW - Perceptual grouping

KW - Visual perception

UR - http://www.scopus.com/inward/record.url?scp=84943574516&partnerID=8YFLogxK

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U2 - 10.1037/a0039540

DO - 10.1037/a0039540

M3 - Article

C2 - 26322548

AN - SCOPUS:84943574516

VL - 122

SP - 575

EP - 597

JO - Psychological Review

JF - Psychological Review

SN - 0033-295X

IS - 4

ER -