{"title":"Placing User-Generated Photo Metadata on a Map","authors":"E. Spyrou, Phivos Mylonas","doi":"10.1109/SMAP.2011.16","DOIUrl":null,"url":null,"abstract":"In this paper we analyze large user photo collections from Flickr in order to select the most appropriate tags to describe a geographical area. We cluster photos based on their latitude and longitude and divide large areas into smaller clusters, which we will refer to as \"geo-clusters\". Geo-clusters have a fixed size and are able to overlap. They do not cover the entire area of interest, omitting parts where no single photo has been geo-tagged at. Within each geo-cluster we analyze all collected textual metadata i.e. the user selected tags of the photos it contains. We are then able to rank them and select the most appropriate that are able to describe landmarks and other places of interest that are contained within. Finally we place these tags on a map to help users to intuitively understand places of interest/visual content at a glance.","PeriodicalId":346975,"journal":{"name":"2011 Sixth International Workshop on Semantic Media Adaptation and Personalization","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 Sixth International Workshop on Semantic Media Adaptation and Personalization","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SMAP.2011.16","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
In this paper we analyze large user photo collections from Flickr in order to select the most appropriate tags to describe a geographical area. We cluster photos based on their latitude and longitude and divide large areas into smaller clusters, which we will refer to as "geo-clusters". Geo-clusters have a fixed size and are able to overlap. They do not cover the entire area of interest, omitting parts where no single photo has been geo-tagged at. Within each geo-cluster we analyze all collected textual metadata i.e. the user selected tags of the photos it contains. We are then able to rank them and select the most appropriate that are able to describe landmarks and other places of interest that are contained within. Finally we place these tags on a map to help users to intuitively understand places of interest/visual content at a glance.