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)

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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.

Keywords: Graph Neural Network; Graphs; Floorplans

Domains: Statistics [stat]; Machine Learning [stat.ML]; Engineering Sciences [physics]; Signal and Image processing; Mathematics [math]; Statistics [math.ST]; Computer Science [cs]; Signal and Image Processing; Computer Science [cs]; Neural and Evolutionary Computing [cs.NE]; Computer Science [cs]; Machine Learning [cs.LG]; Computer Science [cs]; Computers and Society [cs.CY]; Computer Science [cs]; Computer Vision and Pattern Recognition [cs.CV]; Computer Science [cs]; Artificial Intelligence [cs.AI]

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