The amount of media items on the web is increasing tremendously, especially regarding personal media items. To effectively collaborate over and share these massive amounts of media objects, there is a strong need for adequate indexing and search techniques. Trends like social networks, large-storage mobile devices and high-bandwidth networks make peer-to-peer (P2P) information retrieval systems of deep interest.
Hence, resource selection based on compact resource descriptions is used to efficiently determine promising peers w.r.t. a query. To design effective media search applications, multiple search criteria need to be addressed. Subsequently, besides text or visual media content, geospatial data is frequently used.
We propose techniques to summarize and select collections of georeferenced media items in P2P systems. Generally, these summarization techniques can be divided into geometric and space partitioning approaches. This paper presents and evaluates techniques of a third category, hybrid approaches that combine features of geometric and space partitioning techniques.