Intelligent machine vision model for building architectural style classification based on deep learning
by Aaron Rasheed Rababaah; Alaa Musa Rababah
International Journal of Computer Applications in Technology (IJCAT), Vol. 70, No. 1, 2022

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%.

Online publication date: Mon, 03-Apr-2023

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computer Applications in Technology (IJCAT):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email