A Stochastic Grammar of Images

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A Stochastic Grammar of Images (2006)

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by Song-chun Zhu , David Mumford

Venue:	Foundations and Trends in Computer Graphics and Vision
Citations:	120 - 26 self

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@INPROCEEDINGS{Zhu06astochastic,
    author = {Song-chun Zhu and David Mumford},
    title = {A Stochastic Grammar of Images},
    booktitle = {Foundations and Trends in Computer Graphics and Vision},
    year = {2006},
    pages = {259--362}
}

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Abstract

This exploratory paper quests for a stochastic and context sensitive grammar of images. The grammar should achieve the following four objectives and thus serves as a unified framework of representation, learning, and recognition for a large number of object categories. (i) The grammar represents both the hierarchical decompositions from scenes, to objects, parts, primitives and pixels by terminal and non-terminal nodes and the contexts for spatial and functional relations by horizontal links between the nodes. It formulates each object category as the set of all possible valid configurations produced by the grammar. (ii) The grammar is embodied in a simple And–Or graph representation where each Or-node points to alternative sub-configurations and an And-node is decomposed into a number of components. This representation supports recursive top-down/bottom-up procedures for image parsing under the Bayesian framework and make it convenient to scale

Keyphrases

stochastic grammar object category unified framework possible valid configuration exploratory paper quest context sensitive grammar hierarchical decomposition large number functional relation non-terminal node bayesian framework horizontal link recursive top-down bottom-up procedure graph representation

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