Learning Grammars for Architecture-Specific Facade Parsing

Abstract : Parsing facade images requires optimal handcrafted grammar for a given class of buildings. Such a handcrafted grammar is often designed manually by experts. In this paper, we present a novel framework to learn a compact grammar from a set of ground-truth images. To this end, parse trees of ground-truth annotated images are obtained running existing inference algorithms with a simple, very general grammar. From these parse trees, repeated subtrees are sought and merged together to share derivations and produce a grammar with fewer rules. Furthermore, unsupervised clustering is performed on these rules, so that, rules corresponding to the same complex pattern are grouped together leading to a rich compact grammar. Experimental validation and comparison with the state-of-the-art grammar-based methods on four different datasets show that the learned grammar helps in much faster convergence while producing equal or more accurate parsing results compared to handcrafted grammars as well as grammars learned by other methods. Besides, we release a new dataset of facade images following the Art-deco style and demonstrate the general applicability and extreme potential of the proposed framework.
Type de document :
Article dans une revue
International Journal of Computer Vision, Springer Verlag, 2016, 〈10.1007/s11263-016-0887-4〉
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Soumis le : samedi 7 janvier 2017 - 13:34:56
Dernière modification le : jeudi 11 janvier 2018 - 06:20:23



Raghudeep Gadde, Renaud Marlet, Nikos Paragios. Learning Grammars for Architecture-Specific Facade Parsing. International Journal of Computer Vision, Springer Verlag, 2016, 〈10.1007/s11263-016-0887-4〉. 〈hal-01429246〉



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