LAST: Location-Appearance-Semantic-Temporal Clustering Based POI Summarization

When planning a trip, users tend to browse Place-of-Interest (POI) information on the Internet and then depart. Many works aimed at summarizing POIs by visual and textual analysis, while many of them ignored the inter-relationship between different views offered by the community-contributed information. In this paper, we propose a City-POI-LOI (CPL) summarization method to automatically mine POIs from the city-level landmark images. And a Location-Appearance-Semantic-Temporal (LAST) clustering method is proposed to mine the popular viewpoints termed Location-Of-Interest (LOI) in each POI by taking location, appearance, semantic, and temporal feature into consideration. We perform text and image summarization for each LOI, and we further summarize the POIs based on season. We conduct a series of experiments based on DIV400 and ATCF Dataset. Experimental results show the effectiveness of the proposed POI summarization approach.