Shape grammar parsing via Reinforcement Learning

We address shape grammar parsing for facade segmentation using Reinforcement Learning (RL). Shape parsing entails simultaneously optimizing the geometry and the topology (e.g. number of floors) of the facade, so as to optimize the fit of the predicted shape with the responses of pixel-level 'te...

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Bibliographic Details
Published inCVPR 2011 pp. 2273 - 2280
Main Authors Teboul, O., Kokkinos, I., Simon, L., Koutsourakis, P., Paragios, N.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2011
Subjects
Buildings
Grammar
Image color analysis
Labeling
Learning
Markov processes
Shape
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Summary:We address shape grammar parsing for facade segmentation using Reinforcement Learning (RL). Shape parsing entails simultaneously optimizing the geometry and the topology (e.g. number of floors) of the facade, so as to optimize the fit of the predicted shape with the responses of pixel-level 'terminal detectors'. We formulate this problem in terms of a Hierarchical Markov Decision Process, by employing a recursive binary split grammar. This allows us to use RL to efficiently find the optimal parse of a given facade in terms of our shape grammar. Building on the RL paradigm, we exploit state aggregation to speedup computation, and introduce image-driven exploration in RL to accelerate convergence. We achieve state-of-the-art results on facade parsing, with a significant speed-up compared to existing methods, and substantial robustness to initial conditions. We demonstrate that the method can also be applied to interactive segmentation, and to a broad variety of architectural styles.
ISBN:1457703947
9781457703942
ISSN:1063-6919
DOI:10.1109/CVPR.2011.5995319