title: Bottom-up/top-down image parsing with attribute grammar.
abstract: This paper presents a simple attribute graph grammar as a generative representation for made-made scenes, such as buildings, hallways, kitchens, and living rooms, and studies an effective top-down/bottom-up inference algorithm for parsing images in the process of maximizing a Bayesian posterior probability or equivalently minimizing a description length (MDL). Given an input image, the inference algorithm computes (or constructs) a parse graph, which includes a parse tree for the hierarchical decomposition and a number of spatial constraints. In the inference algorithm, the bottom-up step detects an excessive number of rectangles as weighted candidates, which are sorted in certain order and activate top-down predictions of occluded or missing components through the grammar rules. In the experiment, we show that the grammar and top-down inference can largely improve the performance of bottom-up detection.
authors.affiliation: Sarnoff Corporation, Princeton, NJ 08543, USA. hanf@cs.ucla.edu
authors.forename: Feng
authors.initials: F
authors.lastname: Han
authors.forename: Song-Chun
authors.initials: SC
authors.lastname: Zhu
mesh_terms: D000465:Algorithms; D001108:Architecture / Q000379:methods*; D001185:Artificial Intelligence; D003198:Computer Simulation; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated / Q000379:methods*