Interactive Important Social Character Identification from Large Photo Collections In the paper, we describe a mechanism to identify important social characters by analyzing the social structures embedded in photo collections. We first construct a weighted undirected graph from photo collections by examining the coappearance of individuals in photos, wherein the weights of edges are measures of the social closeness of the involved individuals (vertices in the graph). Then a graph clustering algorithm that maximizes the modularity of the graph partition is applied to detect the embedded social clusters. Once the social clusters are identified, we measure individualu0027s contribution to the formation of a social cluster to quantify the social importance of each character in the cluster. To compensate the discrepancy between the userperceived important social characters and the algorithmically computed ones, we propose an interactive browsing scheme to enable viewers quickly identify import social characters that conform to their subjectivity. The effectiveness of the proposed mechanism is demonstrated through experiments on consumer photo collections.

Sparse MultiGraph Ranking towards Social Image Retrieval xe2x80xa0 Graph ranking is one of popular and successful technique for information retrieval. However, conventional graph ranking has two shortcomings when deployed for social image search. First, as social tags are noisy and incomplete, using that, the initial ranked list of images is inaccurate and impacts the following visual reranking. Another tough issue is how to conduct querysensitive image reranking when multiple visual feature sets are available. In this work, we propose a sparse multigraph ranking framework, in which multiple graphs built on different visual features are integrated to simultaneously encode the image ranking. In particular, a sparse constraint is imposed on the fusion of different features, hoping to select a compact yet informative combination of features for different queries. To deal with the highly noisy issue inherent in social tags, a tag refinement solution along with word embedding is utilized to derive the more accurate initial ranking list, which services as the supervision signal for the proposed graph ranking framework. Extensive experimental analyses and evaluations on NUSWIDE dataset demonstrate the proposed method can achieve stateoftheart performance.