Scenery Classification Using Convolutional Neural Network Towards Indonesia Tourism
- 10.2991/978-2-494069-83-1_114How to use a DOI?
- Convolutional Neural Network; Scenery Classification; Pretrained Network
Indonesia is a country that possesses natural wonders and historical buildings, which made Indonesia become one of the popular tourist destination. The scenery classification is a challenging task where the feature distribution from each image may spread. In addition, Indonesian tourism spots are plentiful. In this research, we proposed to utilize deep learning for tourism spot classification using Convolutional Neural Network (CNN) as feature extraction. The dataset consists of different tourism varieties, from man-made objects such as monuments to natural objects such as mountains or beaches. The dataset was self-gathered from the internet and various social media with different angles and does not include any images that dominantly contain people. In addition, in the context of CNN as the basis of feature extractor, we also compared the result with pre-trained CNN architecture trained with Place-365 and ImageNet dataset. The first test was conducted with shallow CNN achieving 48% for the non-augmented dataset and 51% for the augmented dataset. The second test performed with VGG16 and ResNet, combining data augmentation and a pre-trained network. The result reveals data augmentation improves the validation accuracy. Pre-trained VGG16 with Place-365 achieved the highest validation of 90% compared to the other combination. A pretrained network with an augmentation combination improves the model performances significantly by a rough margin.
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Cite this article
TY - CONF AU - Nana Ramadijanti AU - Tita Karlita AU - Achmad Basuki AU - Ulima Inas Shabrina AU - Feri Afrianto AU - Andro Aprila Adiputra AU - Muhammad Dzalhaqi PY - 2022 DA - 2022/12/30 TI - Scenery Classification Using Convolutional Neural Network Towards Indonesia Tourism BT - Proceedings of the International Conference on Applied Science and Technology on Social Science 2022 (iCAST-SS 2022) PB - Atlantis Press SP - 656 EP - 660 SN - 2352-5398 UR - https://doi.org/10.2991/978-2-494069-83-1_114 DO - 10.2991/978-2-494069-83-1_114 ID - Ramadijanti2022 ER -