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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English English |
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Science and Information Organization
2023
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| 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 |
| _version_ | 1825715536472834048 |
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| 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 |
| collection | UMS |
| 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. |
| first_indexed | 2024-03-06T03:28:17Z |
| format | Article |
| id | ums.eprints-38435 |
| institution | Universiti Malaysia Sabah |
| language | English English |
| last_indexed | 2024-03-06T03:28:17Z |
| publishDate | 2023 |
| publisher | Science and Information Organization |
| record_format | dspace |
| spelling | 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 |
| 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 |
| title | 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_short | 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 |
| topic | G154.9-155.8 Travel and state. Tourism QA76.75-76.765 Computer software |
| url | https://eprints.ums.edu.my/id/eprint/38435/1/ABSTRACT.pdf https://eprints.ums.edu.my/id/eprint/38435/2/FULL%20TEXT.pdf |
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