In this paper we investigate generic methods for placing photos uploaded to Flickr on the World map. As primary input for our methods we use the textual annotations provided by the users to predict the single most probable location\ud where the image was taken. Central to our approach is a language model based entirely on the annotations provided by users. We define extensions to improve over the language model using tag-based smoothing and cell-based smoothing, and leveraging spatial ambiguity. Further we demonstrate how to incorporate GeoNames, a large external database of locations. For varying levels of granularity, we are able to place images on a map with at least twice the precision of the state-of-the-art reported in the literature
Social media sources such as Flickr and Twitter continuously generate large amounts of textual infor...
Social media sources such as Flickr and Twitter continuously generate large amounts of textual infor...
Social media sources such as Flickr and Twitter continuously generate large amounts of textual infor...
Knowing the location where a photograph was taken provides us with data that could be useful in a wi...
Knowing the location where a photograph was taken provides us with data that could be useful in a wi...
The topic of automatically assigning geographic coordinates to Web 2.0 resources based on their tags...
Knowing the location where a photograph was taken provides us with data that could be useful in a wi...
The topic of automatically assigning geographic coordinates to Web 2.0 resources based on their tags...
The topic of automatically assigning geographic coordinates to Web 2.0 resources based on their tags...
The task of automatically estimating the location of web resources is of central importance in locat...
The task of automatically estimating the location of web resources is of central importance in locat...
The task of automatically estimating the location of web resources is of central importance in locat...
The task of automatically estimating the location of web resources is of central importance in locat...
Includes bibliographical references (pages 44-47)With the prevalence of smart phones in our daily li...
Abstract—In this paper we analyze large user photo collections from Flickr in order to select the mo...
Social media sources such as Flickr and Twitter continuously generate large amounts of textual infor...
Social media sources such as Flickr and Twitter continuously generate large amounts of textual infor...
Social media sources such as Flickr and Twitter continuously generate large amounts of textual infor...
Knowing the location where a photograph was taken provides us with data that could be useful in a wi...
Knowing the location where a photograph was taken provides us with data that could be useful in a wi...
The topic of automatically assigning geographic coordinates to Web 2.0 resources based on their tags...
Knowing the location where a photograph was taken provides us with data that could be useful in a wi...
The topic of automatically assigning geographic coordinates to Web 2.0 resources based on their tags...
The topic of automatically assigning geographic coordinates to Web 2.0 resources based on their tags...
The task of automatically estimating the location of web resources is of central importance in locat...
The task of automatically estimating the location of web resources is of central importance in locat...
The task of automatically estimating the location of web resources is of central importance in locat...
The task of automatically estimating the location of web resources is of central importance in locat...
Includes bibliographical references (pages 44-47)With the prevalence of smart phones in our daily li...
Abstract—In this paper we analyze large user photo collections from Flickr in order to select the mo...
Social media sources such as Flickr and Twitter continuously generate large amounts of textual infor...
Social media sources such as Flickr and Twitter continuously generate large amounts of textual infor...
Social media sources such as Flickr and Twitter continuously generate large amounts of textual infor...