{"title":"Camera Shooting Location Recommendations for Landmarks in Geo-space","authors":"Y. Zhang, Roger Zimmermann","doi":"10.1109/MASCOTS.2013.25","DOIUrl":null,"url":null,"abstract":"Taking photos of landmarks is a favorite and popular way for travellers to keep memories of places they have visited. Community-contributed photo collections, such as on Flickr, provide us an opportunity to gain a more in-depth understanding of a landmark's visual appeal. While much current research is focusing on recommending which representative photos should be selected from such pervasive photo sources, our work aims to find out where a visitor can capture his or her own, beautiful and personal photo of a queried landmark. We believe that this aspect of helping users to take memorable photos has not been well studied. We propose a method to recommend a list of shooting locations that have the utmost potential to capture appealing photos for a landmark of interest. A Gaussian Mixture Model based clustering approach is applied to the camera locations from an existing photo repository, generating a set of regions each of which covers an area with sufficient semantics, e.g., a route section. The scores and ranks among these camera locations are evaluated through multiple criteria, including their potential for better visual aesthetics, overall social attractiveness, popularity, etc. Additionally, we investigate the temporal characteristics of these locations by considering the spatio-temporal space. A number of different recommendations are generated from these results, such as the best camera positions at different times throughout a single day, or the best visiting time in the same spatial area. Subjective evaluation studies have been conducted, which indicate that our work can generate promising results.","PeriodicalId":385538,"journal":{"name":"2013 IEEE 21st International Symposium on Modelling, Analysis and Simulation of Computer and Telecommunication Systems","volume":"141 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE 21st International Symposium on Modelling, Analysis and Simulation of Computer and Telecommunication Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MASCOTS.2013.25","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
Taking photos of landmarks is a favorite and popular way for travellers to keep memories of places they have visited. Community-contributed photo collections, such as on Flickr, provide us an opportunity to gain a more in-depth understanding of a landmark's visual appeal. While much current research is focusing on recommending which representative photos should be selected from such pervasive photo sources, our work aims to find out where a visitor can capture his or her own, beautiful and personal photo of a queried landmark. We believe that this aspect of helping users to take memorable photos has not been well studied. We propose a method to recommend a list of shooting locations that have the utmost potential to capture appealing photos for a landmark of interest. A Gaussian Mixture Model based clustering approach is applied to the camera locations from an existing photo repository, generating a set of regions each of which covers an area with sufficient semantics, e.g., a route section. The scores and ranks among these camera locations are evaluated through multiple criteria, including their potential for better visual aesthetics, overall social attractiveness, popularity, etc. Additionally, we investigate the temporal characteristics of these locations by considering the spatio-temporal space. A number of different recommendations are generated from these results, such as the best camera positions at different times throughout a single day, or the best visiting time in the same spatial area. Subjective evaluation studies have been conducted, which indicate that our work can generate promising results.