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Landmark Recognition Model for Smart Tourism using Lightweight Deep Learning and Linear Discriminant Analysis

Scene recognition algorithm is crucial for landmark recognition model development. Landmark recognition model is one of the main modules in the intelligent tour guide system architecture for the use of smart tourism industry. However, recognizing the tourist landmarks in the public places are challe...

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Main Authors: Mohd Norhisham Razali, Enurt Owens Nixon Tony, Ag Asri Ag Ibrahim, Rozita Hanapi, Zamhar Iswandono Awang Ismail
Format: Article
Language:English
English
Published: Science and Information Organization 2023
Subjects:
G154.9-155.8 Travel and state. Tourism
QA76.75-76.765 Computer software
Online Access:https://eprints.ums.edu.my/id/eprint/38435/1/ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/38435/2/FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/38435/
https://dx.doi.org/10.14569/IJACSA.2023.0140225
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spelling my.ums.eprints.384352024-03-05T02:30:48Z https://eprints.ums.edu.my/id/eprint/38435/ Landmark Recognition Model for Smart Tourism using Lightweight Deep Learning and Linear Discriminant Analysis Mohd Norhisham Razali Enurt Owens Nixon Tony Ag Asri Ag Ibrahim Rozita Hanapi Zamhar Iswandono Awang Ismail G154.9-155.8 Travel and state. Tourism QA76.75-76.765 Computer software Scene recognition algorithm is crucial for landmark recognition model development. Landmark recognition model is one of the main modules in the intelligent tour guide system architecture for the use of smart tourism industry. However, recognizing the tourist landmarks in the public places are challenging due to the common structure and the complexity of scene objects such as building, monuments and parks. Hence, this study proposes a super lightweight and robust landmark recognition model by using the combination of Convolutional Neural Network (CNN) and Linear Discriminant Analysis (LDA) approaches. The landmark recognition model was evaluated by using several pretrained CNN architectures for feature extraction. Then, several feature selections and machine learning algorithms were also evaluated to produce a super lightweight and robust landmark recognition model. The evaluations were performed on UMS landmark dataset and Scene-15 dataset. The results from the experiments have found that the Efficient Net (EFFNET) with CNN classifier are the best feature extraction and classifier. EFFNET-CNN achieved 100% and 94.26% classification accuracy on UMS-Scene and Scene-15 dataset respectively. Moreover, the feature dimensions created by EFFNet are more compact compared to the other features and even have significantly reduced for more than 90% by using Linear Discriminant Analysis (LDA) without jeopardizing classification performance but yet improved its performance. Science and Information Organization 2023 Article NonPeerReviewed text en https://eprints.ums.edu.my/id/eprint/38435/1/ABSTRACT.pdf text en https://eprints.ums.edu.my/id/eprint/38435/2/FULL%20TEXT.pdf Mohd Norhisham Razali and Enurt Owens Nixon Tony and Ag Asri Ag Ibrahim and Rozita Hanapi and Zamhar Iswandono Awang Ismail (2023) Landmark Recognition Model for Smart Tourism using Lightweight Deep Learning and Linear Discriminant Analysis. International Journal of Advanced Computer Science and Applications (IJACSA), 14. pp. 198-213. ISSN 2156-5570 https://dx.doi.org/10.14569/IJACSA.2023.0140225
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
English
topic G154.9-155.8 Travel and state. Tourism
QA76.75-76.765 Computer software
spellingShingle G154.9-155.8 Travel and state. Tourism
QA76.75-76.765 Computer software
Mohd Norhisham Razali
Enurt Owens Nixon Tony
Ag Asri Ag Ibrahim
Rozita Hanapi
Zamhar Iswandono Awang Ismail
Landmark Recognition Model for Smart Tourism using Lightweight Deep Learning and Linear Discriminant Analysis
description Scene recognition algorithm is crucial for landmark recognition model development. Landmark recognition model is one of the main modules in the intelligent tour guide system architecture for the use of smart tourism industry. However, recognizing the tourist landmarks in the public places are challenging due to the common structure and the complexity of scene objects such as building, monuments and parks. Hence, this study proposes a super lightweight and robust landmark recognition model by using the combination of Convolutional Neural Network (CNN) and Linear Discriminant Analysis (LDA) approaches. The landmark recognition model was evaluated by using several pretrained CNN architectures for feature extraction. Then, several feature selections and machine learning algorithms were also evaluated to produce a super lightweight and robust landmark recognition model. The evaluations were performed on UMS landmark dataset and Scene-15 dataset. The results from the experiments have found that the Efficient Net (EFFNET) with CNN classifier are the best feature extraction and classifier. EFFNET-CNN achieved 100% and 94.26% classification accuracy on UMS-Scene and Scene-15 dataset respectively. Moreover, the feature dimensions created by EFFNet are more compact compared to the other features and even have significantly reduced for more than 90% by using Linear Discriminant Analysis (LDA) without jeopardizing classification performance but yet improved its performance.
format Article
author Mohd Norhisham Razali
Enurt Owens Nixon Tony
Ag Asri Ag Ibrahim
Rozita Hanapi
Zamhar Iswandono Awang Ismail
author_facet Mohd Norhisham Razali
Enurt Owens Nixon Tony
Ag Asri Ag Ibrahim
Rozita Hanapi
Zamhar Iswandono Awang Ismail
author_sort Mohd Norhisham Razali
title Landmark Recognition Model for Smart Tourism using Lightweight Deep Learning and Linear Discriminant Analysis
title_short Landmark Recognition Model for Smart Tourism using Lightweight Deep Learning and Linear Discriminant Analysis
title_full Landmark Recognition Model for Smart Tourism using Lightweight Deep Learning and Linear Discriminant Analysis
title_fullStr Landmark Recognition Model for Smart Tourism using Lightweight Deep Learning and Linear Discriminant Analysis
title_full_unstemmed Landmark Recognition Model for Smart Tourism using Lightweight Deep Learning and Linear Discriminant Analysis
title_sort landmark recognition model for smart tourism using lightweight deep learning and linear discriminant analysis
publisher Science and Information Organization
publishDate 2023
url https://eprints.ums.edu.my/id/eprint/38435/1/ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/38435/2/FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/38435/
https://dx.doi.org/10.14569/IJACSA.2023.0140225
_version_ 1793154683807727616
score 13.211869

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