Bottom-up/Top-Down Image Parsing by Attribute Graph Grammar

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Bottom-up/Top-Down Image Parsing by Attribute Graph Grammar
In this paper, we present an attribute graph grammar for image parsing on scenes with man-made objects, such as buildings, hallways, kitchens, and living rooms. We choose one class of primitives ? 3D planar rectangles projected on images, and six graph grammar production rules. Each production rule not only expands a node into its components, but also includes a number of equations that constrain the attributes of a parent node and those of its children. Thus our graph grammar is context sensitive. The grammar rules are used recursively to produce a large number of objects and patterns in images and thus the whole graph grammar is a type of generative model. The inference algorithm integrates bottom-up rectangle detection which activates topdown prediction using the grammar rules. The final results are validated in a Bayesian framework. The output of the inference is a hierarchical parsing graph with objects, surfaces, rectangles, and their spatial relations. In the inference, the acc...
Feng Han, Song Chun Zhu
Added 15 Oct 2009
Updated 30 Oct 2009
Type Conference
Year 2005
Where ICCV
Authors Feng Han, Song Chun Zhu
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