Who Is the Designer? ARC-100 Database and Benchmark on Architecture Classification

Authors

Yen-Chang Huang^1, , Shih-Yuan Wang², Sze-Teng Liong^{3, *,} , Chieh-En Huang³, Yi-Chen Hsieh³, Hsiang-Yu Wang³, Wen-Hung Lin^3, , Y. S. Gan⁴

¹Department of Applied Mathematics, National University of Tainan, Tainan, Taiwan, Republic of China

²Graduate Institute of Architecture, National Chiao Tung University, Hsinchu, Taiwan Republic of China

³Department of Electronic Engineering, Feng Chia University, Taichung, Taiwan, Republic of China

⁴School of Architecture, Feng Chia University, Taichung, Taiwan, Republic of China

^*Corresponding author. Email: stliong@fcu.edu.tw

DOI

10.2991/ijcis.d.200824.001How to use a DOI?

Keywords

Architecture; Building; Classification; Segmentation; CNN

Abstract

Architecture is about evolution, there exist many types of architectural styles that depend on the geography, traditions, and culture of the particular regions. An architectural designer may have a similar preference in creating the new architectural building, which can be easily recognized from the physical attributes and characteristics. This paper performs an architect classification based on the outward appearance of the building. An architecture database with 100 images (ARC-100) that have balanced class distribution is constructed. Among the architectural buildings, the best performance is 71% for 5-class classification. Convolutional neural networks (CNNs) have demonstrated breakthrough performance on various classification tasks in recent studies, and even outperform human experts in specific tasks. Thus, for the baseline evaluation, multiple pretrained CNN models are employed with slight modifications. Prior to the feature extraction and classification processes, the removal of background noise is performed using two approaches: manually and automatically. The former approach requires high human intervention, while the latter utilizes the cutting-edge object segmentation technology, namely mask regional convolutional neural network (R-CNN). The illustration of the experiment training progress and the confusion matrix are reported, to allow further interpretation and analysis for the model trained. Notably, this is the first work that performs automatic classification based on architectural styles. This framework can be used to improve the cultural understanding and practices in providing education for holistic development and enhance the learning experience and progressions from an aesthetic perspective.

Copyright

© 2020 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)
View full text (HTML)

TY  - JOUR
AU  - Yen-Chang Huang
AU  - Shih-Yuan Wang
AU  - Sze-Teng Liong
AU  - Chieh-En Huang
AU  - Yi-Chen Hsieh
AU  - Hsiang-Yu Wang
AU  - Wen-Hung Lin
AU  - Y. S. Gan
PY  - 2020
DA  - 2020/09/02
TI  - Who Is the Designer? ARC-100 Database and Benchmark on Architecture Classification
JO  - International Journal of Computational Intelligence Systems
SP  - 1305
EP  - 1314
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200824.001
DO  - 10.2991/ijcis.d.200824.001
ID  - Huang2020
ER  -