TY - GEN
T1 - Comparing classification methods for the building plan components
AU - Wilfing, Daniel
AU - Krauss, Oliver
N1 - Publisher Copyright:
© 2020 The Authors.
PY - 2020
Y1 - 2020
N2 - The classification methods Histogram of Oriented Gradients, Bag of Features, Support Vector Machines and Neural Networks are evaluated to find a fitting solution for the automatic classification of building plan components. These components feature shapes with little features and minor differences. After processing the building plans for the classification, feature analysis methods, as well as machine learning based approaches, are tested. The results of the classification methods are compared and the behaviors of the classification methods are analyzed. First results have shown, that neural network classification using line data extracted via Hough transformation and additional calculations surpass other classification methods tested in this work. It was found that the basic structure of building plan components can be detected with neural networks, but further improvements have to be made, if only a single classification process is to be relied on. In the future this work will be used to create 3D building models from 2D plans and enable agent based simulation in the models.
AB - The classification methods Histogram of Oriented Gradients, Bag of Features, Support Vector Machines and Neural Networks are evaluated to find a fitting solution for the automatic classification of building plan components. These components feature shapes with little features and minor differences. After processing the building plans for the classification, feature analysis methods, as well as machine learning based approaches, are tested. The results of the classification methods are compared and the behaviors of the classification methods are analyzed. First results have shown, that neural network classification using line data extracted via Hough transformation and additional calculations surpass other classification methods tested in this work. It was found that the basic structure of building plan components can be detected with neural networks, but further improvements have to be made, if only a single classification process is to be relied on. In the future this work will be used to create 3D building models from 2D plans and enable agent based simulation in the models.
KW - Building plan
KW - Classification
KW - Feature analysis
KW - Machine learning
KW - Modeling & Simulation
UR - http://www.scopus.com/inward/record.url?scp=85097707911&partnerID=8YFLogxK
U2 - 10.46354/i3m.2020.emss.021
DO - 10.46354/i3m.2020.emss.021
M3 - Conference contribution
AN - SCOPUS:85097707911
T3 - 32nd European Modeling and Simulation Symposium, EMSS 2020
SP - 154
EP - 160
BT - 32nd European Modeling and Simulation Symposium, EMSS 2020
A2 - Affenzeller, Michael
A2 - Bruzzone, Agostino G.
A2 - Longo, Francesco
A2 - Petrillo, Antonella
PB - DIME UNIVERSITY OF GENOA
T2 - 32nd European Modeling and Simulation Symposium, EMSS 2020
Y2 - 16 September 2020 through 18 September 2020
ER -