Tourism Category Classification on Image Sharing Services Through Estimation of Existence of Reliable Results

Saito, Naoki; Ogawa, Takahiro; Asamizu, Satoshi; Haseyama, Miki

doi:10.1145/3206025.3206085

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…In addition, as the process of sharing the images on SNS consists of continually producing and reproducing touristic images, we are able to ascertain perceptions and trends of representative sightseeing elements and locations by analyzing the images uploaded on SNS. Furthermore, this process contributes to the basic research on tourism in relation to discovering, developing, and improving sightseeing attractions (Saito et al, 2018). We think that it is possible for us to analyze broader scope with more extracted information in tandem with preexisting methodologies of spatial data analysis because geo-tagged photos contain locational information.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Tourists’ Image of Seoul with Geotagged Photos using Convolutional Neural Networks

Kim

Kang

Park

et al. 2019

Proc. Int. Cartogr. Assoc.

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In this study we aim to analyze the urban image of Seoul that tourists feel through the photos uploaded on Flickr, which is one of Social Network Service (SNS) platforms that people can share Geo-tagged photos. We first categorize the photos uploaded on the site by tourists and then performed the image mining by utilizing Convolutional Neural Network (CNN), which is one of the artificial neural networks with deep learning capability. In this study we are able to find out that tourists are interested in old palaces, historical monuments, stores, food, etc. in which are considered to be the signatured sightseeing elements in Seoul. Those key elements are differentiated from the major sightseeing attractions within Seoul. The purpose of this study is two folds: First, we analyze the image of Seoul by applying the technology of image mining with the photos uploaded on Flickr by tourists. Second, we draw some significant sightseeing factors by region of attraction where tourists prefer to visit within Seoul.

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Section: Introductionmentioning

confidence: 99%

Analysis of Tourists’ Image of Seoul with Geotagged Photos using Convolutional Neural Networks

Kim

Kang

Park

et al. 2019

Proc. Int. Cartogr. Assoc.

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show abstract

“…In addition, as the touristic images on SNS can be continually produced and reproduced, we are able to ascertain the perceptions and the trends of representative sightseeing elements and locations by analyzing the images uploaded on SNS. Furthermore, this process contributes to the basic research on tourism for discovering, developing, and improving sightseeing attractions [3].…”

Section: Introductionmentioning

confidence: 99%

Understanding tourists’ urban images with geotagged photos using convolutional neural networks

et al. 2019

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This study aims to track down representative images and elements of sightseeing attractions by analyzing the photos uploaded on Flickr by Seoul tourists with the image mining technique. For this purpose, we crawled the photos uploaded on Flickr and classified users into residents and tourists; drew 11 region of attractions (RoA) in Seoul by analyzing the spatial density of the photos; classified the photos into 1000 categories and then 14 categories by grouping 1000 categories by utilizing Inception V3 model; analyzed the characteristics of the photo image by RoA. Key findings of this study are that tourists are interested in old palaces, historical monuments, stores, food, etc. and those key elements are distinguished from the major sightseeing attractions in Seoul. More specifically, tourists are more interested in palaces and cultural assets in Jongno and Namsan, food and restaurants in Shinchon, Hongdae, Itaewon, Yeouido, Garosu-gil, and Apgujeong, war monuments or specific artifacts in War Memorial and the National Museum of Korea, facilities, temples, and pictures of cultural properties in Samsung Station, and toyshops in Jamsil. This study is meaningful in three folds: first, it tries to analyze urban image through the photos posted on SNS by tourists. Second, it uses deep learning technique to analyze the photos. Third, it classifies and analyzes the whole photos posted by Seoul tourists while most of other researches focus on only specific objects. However, this study has a limitation because the Inception v3 model which has been used in this research is a pre-trained model created by training the ImageNet data. In future research, it is necessary to classify photo categories according to the purpose of tourism and retrain the model by creating new training data set focusing on elements of Korea.

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Cyclists' Avoidance Behavior Detection Based on a Multimodal Approach for Road Management

2021

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Tourism Category Classification on Image Sharing Services Through Estimation of Existence of Reliable Results

Cited by 3 publications

References 12 publications

Analysis of Tourists’ Image of Seoul with Geotagged Photos using Convolutional Neural Networks

Analysis of Tourists’ Image of Seoul with Geotagged Photos using Convolutional Neural Networks

Understanding tourists’ urban images with geotagged photos using convolutional neural networks

Cyclists' Avoidance Behavior Detection Based on a Multimodal Approach for Road Management

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