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dc.contributor.authorHuang, Yen-Changen_US
dc.contributor.authorWang, Shih-Yuanen_US
dc.contributor.authorLiong, Sze-Tengen_US
dc.contributor.authorHuang, Chieh-Enen_US
dc.contributor.authorHsieh, Yi-Chenen_US
dc.contributor.authorWang, Hsiang-Yuen_US
dc.contributor.authorLin, Wen-Hungen_US
dc.contributor.authorGan, Y. S.en_US
dc.date.accessioned2020-10-05T02:01:58Z-
dc.date.available2020-10-05T02:01:58Z-
dc.date.issued2020-01-01en_US
dc.identifier.issn1875-6891en_US
dc.identifier.urihttp://dx.doi.org/10.2991/ijcis.d.200824.001en_US
dc.identifier.urihttp://hdl.handle.net/11536/155393-
dc.description.abstractArchitecture 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. (C) 2020 The Authors. Published by Atlantis Press B.V.en_US
dc.language.isoen_USen_US
dc.subjectArchitectureen_US
dc.subjectBuildingen_US
dc.subjectClassificationen_US
dc.subjectSegmentationen_US
dc.subjectCNNen_US
dc.titleWho Is the Designer? ARC-100 Database and Benchmark on Architecture Classificationen_US
dc.typeArticleen_US
dc.identifier.doi10.2991/ijcis.d.200824.001en_US
dc.identifier.journalINTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMSen_US
dc.citation.volume13en_US
dc.citation.issue1en_US
dc.citation.spage1305en_US
dc.citation.epage1314en_US
dc.contributor.department建築研究所zh_TW
dc.contributor.departmentInstitude of Architectureen_US
dc.identifier.wosnumberWOS:000569520100002en_US
dc.citation.woscount0en_US
Appears in Collections:Articles