rich location-driven tag cloud suggestions based on public, community, and personal sources
Recent research has shown the power of geotagging for many multimedia applications. In this paper, we present an integrated and intuitive system for suggesting location-driven tags for a geotagged photo. Potential tags from multiple sources are extracted, including points of interest (POI) tags from a public Geographic Names Information System (GNIS) database, community tags from Flickr pictures, and personal tags shared through user's own, family and friends' photo collections. To increase the effectiveness of GNIS POI tags, bags of place name tags are first retrieved and then re-ranked using a combined tf-idf and spatial distance criteria. The community tags from photos taken in the vicinity of the input geotagged photo are ranked according to distance and visual similarity to the input photo. Personal tags from other personally related photos inherently carry a significant weight due to their high relevance than both the generic place name tags and community tags, and are ranked by weights decaying over time and distance differences. Finally, a rich set of the most relevant location-driven tags is presented to the user in the form of individual tags clouds under the three mentioned source categories. The tag clouds act as intuitive suggestions for tagging an input image. Preliminary user evaluation has revealed the respective benefits of the three categories and shown the effectiveness of the integrated tag suggestion system.
tag cloud geotagging nearest neighbors visual matching