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Active learning with uncertainty sampling has been popularly employed in implementing interactive video search, due to its promise to reduce labeling efforts. However, since the ultimate goal of interactive search is to find as many relevant shots as possible, the purely explorative learning strategy always places conventional active learning in a dilemma(More)
Modeling the in-class student social networks is a highly desired goal in educational literature. However, due to the difficulty to collect social data, most of the conventional studies can only be conducted in a qualitative way on a small-scale of dataset obtained through questionnaires or interviews. We propose to solve the problems of data collection,(More)
Near-duplicate retrieval (NDR) in merchandize images is of great importance to a lot of online applications on e-Commerce websites. In those applications where the requirement of response time is critical, however, the conventional techniques developed for a general purpose NDR are limited, because expensive post-processing like spatial verification or(More)
In this paper, we have proposed a novel method, which utilizes the contextual relationship among visual words for reducing the quantization errors in near-duplicate image retrieval (NDR). Instead of following the track of conventional NDR techniques, which usually search new solutions by borrowing ideas from the text domain, we propose to model the problem(More)
Conventional active learning approaches for interactive video/image retrieval usually assume the query distribution is unknown, as it is difficult to estimate with only a limited number of labeled instances available. Thus, it is easy to put the system in a dilemma whether to explore the feature space in uncertain areas for a better understanding of the(More)
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