  Media sharing applications, such as Flickr and Panoramio, contain a large
amount of pictures related to real life events. For this reason, the
development of effective methods to retrieve these pictures is important, but
still a challenging task. Recognizing this importance, and to improve the
retrieval effectiveness of tag-based event retrieval systems, we propose a new
method to extract a set of geographical tag features from raw geo-spatial
profiles of user tags. The main idea is to use these features to select the
best expansion terms in a machine learning-based query expansion approach.
Specifically, we apply rigorous statistical exploratory analysis of spatial
point patterns to extract the geo-spatial features. We use the features both to
summarize the spatial characteristics of the spatial distribution of a single
term, and to determine the similarity between the spatial profiles of two terms
-- i.e., term-to-term spatial similarity. To further improve our approach, we
investigate the effect of combining our geo-spatial features with temporal
features on choosing the expansion terms. To evaluate our method, we perform
several experiments, including well-known feature analyses. Such analyses show
how much our proposed geo-spatial features contribute to improve the overall
retrieval performance. The results from our experiments demonstrate the
effectiveness and viability of our method.
