Clustering Geo-tagged Photographs using Dynamic Programming


This paper describes methods for clustering photos that include both time stamps and location coordinates. We present versions of a two part method that first detects clusters using time and location information independently. These candidate clusters partition the set of time-ordered photos. A subset of the candidate clusters is selected by an efficient dynamic programming
procedure to optimize a cost function. We propose several cost functions to design clusterings that are coherent in space, time, or both. One set of cost functions minimizes inter-photo distances directly. A second set maximizes an information measure to select clusterings for consistency in both time and space across scale.