Title: Intelligent machine vision model for building architectural style classification based on deep learning
Authors: Aaron Rasheed Rababaah; Alaa Musa Rababah
Addresses: College of Engineering and Applied Sciences, American University of Kuwait, Salmia, Kuwait ' University of Jordan, Amman, Kuwait
Abstract: 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 optimised. 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 data set was utilised to train and validate the proposed model. The data set 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%.
Keywords: architectural styles classification; machine intelligence; machine vision; deep learning; feature extraction; impact of number kernels/features.
International Journal of Computer Applications in Technology, 2022 Vol.70 No.1, pp.11 - 21
Received: 09 Dec 2021
Accepted: 12 Feb 2022
Published online: 03 Apr 2023 *