NDLI: DeepDBSCAN: Deep Density-Based Clustering for Geo-Tagged Photos

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DeepDBSCAN: Deep Density-Based Clustering for Geo-Tagged Photos

Content Provider	MDPI
Author	Park, Jang You Ryu, Dong June Nam, Kwang Woo Jang, Insung Jang, Minseok Lee, Yonsik
Copyright Year	2021
Description	Density-based clustering algorithms have been the most commonly used algorithms for discovering regions and points of interest in cities using global positioning system (GPS) information in geo-tagged photos. However, users sometimes find more specific areas of interest using real objects captured in pictures. Recent advances in deep learning technology make it possible to recognize these objects in photos. However, since deep learning detection is a very time-consuming task, simply combining deep learning detection with density-based clustering is very costly. In this paper, we propose a novel algorithm supporting deep content and density-based clustering, called deep density-based spatial clustering of applications with noise (DeepDBSCAN). DeepDBSCAN incorporates object detection by deep learning into the density clustering algorithm using the nearest neighbor graph technique. Additionally, this supports a graph-based reduction algorithm that reduces the number of deep detections. We performed experiments with pictures shared by users on Flickr and compared the performance of multiple algorithms to demonstrate the excellence of the proposed algorithm.
Starting Page	548
e-ISSN	22209964
DOI	10.3390/ijgi10080548
Journal	ISPRS International Journal of Geo-Information
Issue Number	8
Volume Number	10
Language	English
Publisher	MDPI
Publisher Date	2021-08-14
Access Restriction	Open
Subject Keyword	ISPRS International Journal of Geo-Information Isprs International Journal of Geo-information Artificial Intelligence Density-based Clustering Object Detection Geo-tagged Photos Dbscan Big Data Crowdsourcing
Content Type	Text
Resource Type	Article

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