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Automatic TV commercial detection has become an indispensable part of content-based video analysis technique due to the explosive growth in TV commercial volume. In this paper, a multi-modal (i.e. visual, audio and textual modalities) commercial digesting scheme is proposed to alleviate two challenges in commercial detection, which are the generation of mid-level semantic descriptor and the application...
Natural scene categorization (NSC) is an important and challenging task. Several state-of-the-art NSC systems use a codebook of visual terms to characterize images with the statistic of visual word counts. However, some kind of codebook generally tends to be more favorable for characterizing a special scene category, which takes either flat property or salient one. To obtain the good tradeoff performance...
Automatic image annotation (AIA) is a promising way to improve the performance of image retrieval. In this paper, we propose a novel AIA scheme based on multiple-instance learning (MIL). By introducing the minimum reference set (MRS) into MIL (denoted by MRS-MIL), the positive instances (i.e. regions in images) embedded in the positive bags (i.e. images) can be picked out via reliable inferring for...
The goal of this paper is to develop a learning strategy for interactive video search that can effectively mitigate the burden on users without decreasing search performance. Taking SVM as underlying learner, a cooperative training strategy is proposed for learning a ranking function, in which semi-supervised learning procedure is started with a combination of a few positive training seeds and a relative...
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