Statistical Modeling and Conceptualization of Visual Patterns

doi:10.1109/TPAMI.2003.1201820

Paper

Statistical Modeling and Conceptualization of Visual Patterns

Published Jun 1, 2003 · Song-Chun Zhu

IEEE Trans. Pattern Anal. Mach. Intell.

Q1 SJR score

135

Citations

4

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Abstract

Natural images contain an overwhelming number of visual patterns generated by diverse stochastic processes. Defining and modeling these patterns is of fundamental importance for generic vision tasks, such as perceptual organization, segmentation, and recognition. The objective of this epistemological paper is to summarize various threads of research in the literature and to pursue a unified framework for conceptualization, modeling, learning, and computing visual patterns. This paper starts with reviewing four research streams: 1) the study of image statistics, 2) the analysis of image components, 3) the grouping of image elements, and 4) the modeling of visual patterns. The models from these research streams are then divided into four categories according to their semantic structures: 1) descriptive models, i.e., Markov random fields (MRF) or Gibbs, 2) variants of descriptive models (causal MRF and "pseudodescriptive" models), 3) generative models, and 4) discriminative models. The objectives, principles, theories, and typical models are reviewed in each category and the relationships between the four types of models are studied. Two central themes emerge from the relationship studies. 1) In representation, the integration of descriptive and generative models is the future direction for statistical modeling and should lead to richer and more advanced classes of vision models. 2) To make visual models computationally tractable, discriminative models are used as computational heuristics for inferring generative models. Thus, the roles of four types of models are clarified.

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Key takeawayIntegrating descriptive and generative models is the future direction for statistical modeling, leading to richer and more advanced vision models, while discriminative models serve as computational heuristics for inferring generative models.

PopulationOlder adults (50-71 years)

Sample size24

MethodsObservational

OutcomesBody Mass Index projections

ResultsSocial networks mitigate obesity in older groups.

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Citations

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