An incremental probabilistic model for temporal theme analysis of landmarks

Social media sites (e.g., Flickr) generate a huge amount of landmark photos with temporal information in the real-world, such as the photos describing the events happening near landmarks, and those showing different seasonal sceneries. Analyzing this temporal information of landmarks can benefit various applications, such as landmark timeline construction and tour recommendation. In this paper, we propose a novel Incremental Spatio-Temporal Theme Model (ISTTM), which can incrementally mine temporal themes that characterize the temporal information of landmarks, by differentiating them from the other three kinds of themes, i.e., general themes shared by most of all landmarks, local themes related to certain landmarks and the background theme including non-informative content. ISTTM works in an online way and is capable of selectively processing the updates of the distributions on different types of themes. Based on the proposed ISTTM, we present a framework, namely Temporal Theme Analysis for Landmarks (TTAL), which enables both periodic theme detection from discovered temporal themes and temporal theme visualization by selecting the relevant photos. We have conducted experiments on a large-scale landmark dataset from Flickr. Qualitative and quantitative evaluation results demonstrate the effectiveness of the ISTTM as well as the TTAL framework.