Open AccessSegmentation of 3D meshes combining the artificial neural network classifier and the spectral clustering
Author(s) -
Fatima Rafii Zakani,
Mohcine Bouksim,
Khadija Arhid,
Azah Mohamed,
Taoufiq Gadi
Publication year - 2018
Publication title -
kompʹûternaâ optika
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.491
H-Index - 29
eISSN - 2412-6179
pISSN - 0134-2452
DOI - 10.18287/2412-6179-2018-42-2-312-319
Subject(s) - segmentation , artificial intelligence , computer science , pattern recognition (psychology) , cluster analysis , artificial neural network , spectral clustering , polygon mesh , classifier (uml) , ground truth , benchmark (surveying) , computer graphics (images) , geodesy , geography
3D mesh segmentation has become an essential step in many applications in 3D shape analysis. In this paper, a new segmentation method is proposed based on a learning approach using the artificial neural networks classifier and the spectral clustering for segmentation. Firstly, a training step is done using the artificial neural network trained on existing segmentation, taken from the ground truth segmentation (done by humane operators) available in the benchmark proposed by Chen et al. to extract the candidate boundaries of a given 3D-model based on a set of geometric criteria. Then, we use this resulted knowledge to construct a new connectivity of the mesh and use the spectral clustering method to segment the 3D mesh into significant parts. Our approach was evaluated using different evaluation metrics. The experiments confirm that the proposed method yields significantly good results and outperforms some of the competitive segmentation methods in the literature.