Abstract
Many photographs with location information are posted on the LBSN, and these subjects well represent the characteristics of the shooting location, so they are widely used for hotspot extraction. However, only famous tourist spots with a large number of posts are mentioned, and there is a tendency that the potential spots are not be focused on. In this study, we propose a method to extract POIs with a common feature distribution in photographs as areas and to characterize the areas by topics that appear more often in the area than in the surrounding area. Specifically, the minimum spanning tree is constructed from the position information of the posted photos, and the target region is divided into sub-areas by cutting the tree according to the feature distribution of the posted photo calculated by VGG16. An evaluation experiment using photographs taken in Tokyo shows that it outperforms existing methods in terms of semantic cohesiveness with similar feature distribution and positional cohesiveness with close shooting positions. Furthermore, compared with the feature distribution of the entire target region, the evaluation is made from the viewpoint of whether each divided area has a characteristic topic, and the photographs peculiar to each area are confirmed.
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Notes
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Due to space limitations, representative photographs of all 50 areas could not be displayed, so 12 areas were selected at random.
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This material is based upon work supported by JSPS Grant-in-Aid for Scientific Research (C) (No.22K12279) and Early-Career Scientists (No.19K20417).
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Fushimi, T., Matsuo, E. (2022). Extracting Characteristic Areas Based on Topic Distribution over Proximity Tree. In: Pacheco, D., Teixeira, A.S., Barbosa, H., Menezes, R., Mangioni, G. (eds) Complex Networks XIII. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-031-17658-6_9
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