Lixian Huang

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A churn is defined as the loss of a user in an online social network (OSN). Detecting and analyzing user churn at an early stage helps to provide timely delivery of retention solutions (e.g., interventions, customized services, and better user interfaces) that are useful for preventing users from churning. In this paper we develop a prediction model based(More)
A hybrid scheme which uses both feature-based and intensity-based methods is proposed. In particular, an edge-based image registration approach is developed to guide the intensity-based registration which uses optical flow estimation. The idea of coarse-to-fine multi-scale iterative refinement is also utilized. The combination of these different methods(More)
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