Close & closer: social cluster and closeness from photo collections

We investigate the discovery of social clusters from consumer photo collections. People's participation in various social activities is the base on which social clusters are formed. The photos that record those social activities can reflect the social structure of people to a certain degree, depending on the extent of coverage of the photos on the social activities. In this paper, we propose a scheme to construct a weighted undirected graph from photo collections by examining the co-appearance of individuals in photos, wherein the weights of edges are measures of the social closeness of the involved individuals (vertices in the graph). We further apply a graph clustering algorithm that maximizes the modularity of the graph partition to detect the embedded social clusters. The experiment results demonstrate that this scheme can reveal the social cluster with high precision rate. In addition, we also introduce a few photo management capabilities enabled by the social graph and discovered social clusters.