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Symbols Detection and Classification using Graph Neural Networks

Guillaume Renton 1 Muhammet Balcilar 1 Pierre Héroux 1 Benoit Gaüzère 1 Paul Honeine 1 Sébastien Adam 1 
1 DocApp - LITIS - Equipe Apprentissage
LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes
Abstract : In this paper, we propose a method to both extract and classify symbols in floorplan images. This method relies on the very recent developments of Graph Neural Networks (GNN). In the proposed approach, floorplan images are first converted into Region Adjacency Graphs (RAGs). In order to achieve both classification and extraction, two different GNNs are used. The first one aims at classifying each node of the graph while the second targets the extraction of clusters corresponding to symbols. In both cases, the model is able to take into account edge features. Each model is firstly evaluated independently before combining both tasks simultaneously, increasing the quickness of the results.
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https://hal-normandie-univ.archives-ouvertes.fr/hal-03410511
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Submitted on : Thursday, December 23, 2021 - 10:55:37 PM
Last modification on : Wednesday, March 2, 2022 - 10:10:12 AM

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Guillaume Renton, Muhammet Balcilar, Pierre Héroux, Benoit Gaüzère, Paul Honeine, et al.. Symbols Detection and Classification using Graph Neural Networks. Pattern Recognition Letters, Elsevier, 2021, ⟨10.1016/j.patrec.2021.09.020⟩. ⟨hal-03410511⟩

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