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Binary relevance (BR) is a well-known framework for multi-label classification. It decomposes multi-label classification into binary (one-vs-rest) classification subproblems, one for each label. The BR approach is a simple and straightforward way for multi-label classification, but it still has several drawbacks. First, it does not consider label correlations. Second, each binary classifier may suffer...
With the proliferation of diversified social network services, understanding how the influence is propagated helps us better understand the network evolution mechanism and the social impact of different kinds of information. Existing models are mostly built on the static network structure. They fail to catch the temporal dynamic property of social network. In this paper, we design a new kind of latent...
Interactions among pedestrians usually play an important role in understanding crowd behavior. However, there are great challenges, such as occlusions, motion, and appearance variance, on accurate analysis of pedestrian interactions. In this paper, we introduce a novel social attribute-aware force model (SAFM) for detection of abnormal crowd events. The proposed model incorporates social characteristics...
The appearances of the tracked object and its surrounding background usually change during tracking. As for tracking methods using subspace analysis, fixed subspace basis tends to cause tracking failure. In this paper, a novel tracking method is proposed by using incremental 2D-LDA learning and Bayes inference. Incremental 2D-LDA formulates object tracking as online classification between foreground...
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