Scenery Classification Using Convolutional Neural Network Towards Indonesia Tourism

DOI

10.2991/978-2-494069-83-1_114How to use a DOI?

Keywords

Convolutional Neural Network; Scenery Classification; Pretrained Network

Abstract

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.

Copyright

© 2022 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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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  -