Exploring and visualizing tag relationships in photo sharing websites based on distributional representations
Marie Katsurai, Miki Haseyama · 2013
This paper presents a method for exploring and visualizing tag relationships in photo sharing websites based on distributional representations of tags. First, we find a representative distribution of a tag, which is summarized by the mean and covariance, using features of tagged photos. This distributional representation can jointly consider the semantic meaning of tags and their abstraction levels. Then, based on the representative distributions, we derive two kinds of semantic measures on tag relationships. The extracted information is visualized in a graphical network to facilitate the understanding of tag usage. Experiments conducted using tagged photos collected from Flickr show that our tag network is more coherent to human cognition than other networks constructed by conventional methods.