Automated social hierarchy detection through email network analysis
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Measuring social networks with digital photograph collections
Proceedings of the nineteenth ACM conference on Hypertext and hypermedia
Close & closer: discover social relationship from photo collections
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Consumer image retrieval by estimating relation tree from family photo collections
Proceedings of the ACM International Conference on Image and Video Retrieval
Improving face clustering using social context
Proceedings of the international conference on Multimedia
Semantic analysis and retrieval in personal and social photo collections
Multimedia Tools and Applications
Social community detection from photo collections using Bayesian overlapping subspace clustering
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part II
Dynamic estimation of family relations from photos
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part II
Mining social networks and their visual semantics from social photos
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
Modeling social strength in social media community via kernel-based learning
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Predicting participants in public events using stock photos
Proceedings of the 20th ACM international conference on Multimedia
Discovering relationship types between users using profiles and shared photos in a social network
Multimedia Tools and Applications
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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.