Extract Knowledge About Geo-Location Using Context and Content Information of Geo-Tagged Social Media

Recent years have witnessed a phenomenal growth of social sharing websites. These social sharing web sites host a large volume of geo-referenced and community contributed media resources. However, due to the size and inability of computers to understand the content of the images, it is inherently difficult to organize, browse or search these collections. The work proposed here addresses an interesting task of organizing these geo-referenced media on Flickr to generate visual and thematic summarization of specified geo-location. The major challenge is: how to use the unstructured and unrestricted community contributed media and annotations to generate knowledge? We propose the modified PLSA algorithm to generate representative sets of images and tags for given geo-location. We have used location metadata, tags associated with images, and the visual features of images to understand context and content of images. To deal with social tagging accuracy problem, we consider ternary interrelations and multiple intra-relations among user, image and tag and model the relations using HOSVD. The novel visualization scheme summarizes the geo-location with a rich display landscape and provides location description using location representative tags. Experiments are performed on geo-tagged Flickr images for various geo-locations. The experimental results have validated the proposed method.