Intelligent Machine Vision Model for Building Architectural Style Classification based on Deep Learning

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Rababaah, Aaron
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Journal Article
This paper presents an intelligent model for building architectural style classification. Image classification of architectural style is challenging to traditional machine vision methods. The main challenge in these systems is the feature extraction phase as there are many visual features in these styles that need to be extracted, refined and optimized. All these operations are done at the researcher discretion in traditional Machine Learning (ML) models. The advancements of ML to Deep Learning (DL) made automation of all the challenging operations possible. We constructed a machine vision model based on DL to investigate the effectiveness of DL in the classification problem at hand. A publicly available annotated dataset was used to train and validate the proposed model. The dataset consists of more than 5000 images of eight different architectural styles. The experimental results showed that the proposed model is reliable as it produced a classification accuracy of 95.44%.
Rababaah, A.(2022). Intelligent Machine Vision Model for Building Architectural StyleClassification based on Deep Learning. International Journal of ComputerApplications in Technology