Clustering geo-tagged photo collections using dynamic programming

Authors:
Matthew L. Cooper
Affiliations:
FX Palo Alto Laboratory, Palo Alto, CA, USA
Venue:
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Year:
2011

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Abstract

This paper describes methods for clustering photos that possess both time stamps and geographical coordinates as metadata. We present a two part method that first analyzes photos' time and location information to independently partition the photos into multiple clusterings. A subset of the detected clusters is then selected for the final photo clustering using an efficient dynamic programming procedure that optimizes a clustering fitness score. We propose fitness measures to produce clusterings that are coherent in space, time, or both. One group of scores directly measures within-cluster inter-photo distances. A second set of scores measures clusters' consistency with the reference clusterings. We present experiments that validate our method using multiple data sets.