Extracting Representative Images of Tourist Attractions from Flickr by Combining an Improved Cluster Method and Multiple Deep Learning Models
Bibliographic Data
| ID | 22034092 |
|---|---|
| Authors | Shanshan Han (0000-0002-7382-6028, Wuhan University), Fu Ren (0000-0002-5460-9909, Wuhan University), Qingyun Du (0000-0003-4615-2029, Wuhan University, corresponding author), Dawei Gui (0000-0002-9176-8415, Wuhan University, corresponding author) |
| Year | 2020 |
| Volume | 9 |
| Issue | 2 |
| Pages | 81 |
| Publication date | 2020-01-31 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi9020081 |
| OpenAlex | W3003170025 |
| Language | EN |
| Citations received | 3 |
| References cited | 39 |
Extracting representative images of tourist attractions from geotagged photos is beneficial to many fields in tourist management, such as applications in touristic information systems. This task usually begins with clustering to extract tourist attractions from raw coordinates in geotagged photos. However, most existing cluster methods are limited in the accuracy and granularity of the places of interest, as well as in detecting distinct tags, due to its primary consideration of spatial relationships. After clustering, the challenge still exists for the task of extracting representative images within the geotagged base image data, because of the existence of noisy photos occupied by a large area proportion of humans and unrelated objects. In this paper, we propose a framework containing an improved cluster method and multiple neural network models to extract representative images of tourist attractions. We first propose a novel time- and user-constrained density-joinable cluster method (TU-DJ-Cluster), specific to photos with similar geotags to detect place-relevant tags. Then we merge and extend the clusters according to the similarity between pairs of tag embeddings, as trained from Word2Vec. Based on the clustering result, we filter noise images with Multilayer Perceptron and a single-shot multibox detector model, and further select representative images with the deep ranking model. We select Beijing as the study area. The quantitative and qualitative analysis, as well as the questionnaire results obtained from real-life tourists, demonstrate the effectiveness of this framework
Cluster analysis · Data mining · Geography · Geotagging · Information retrieval · Tourism · Word2vec · Advanced Image and Video Retrieval Techniques · Computer Science · Diverse Aspects of Tourism Research · Human Mobility and Location-Based Analysis · Artificial Intelligence
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A Tourist Attraction Recommendation Model Fusing Spatial, Temporal, and Visual Embeddings for Flickr-Geotagged Photos
Automatic Classification of Photos by Tourist Attractions Using Deep Learning Model and Image Feature Vector Clustering
| Unique citing works | 3 |
|---|---|
| Citations per year | 0,6 |
| Citation span | 2021 - 2025 (5) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 3 |