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In this paper, we propose a novel error concealment method based on multiscale patch clustering and low-rank minimization. In order to collect more reliable patches to form a genuine low-rank matrix, an image pyramid is formed utilizing an effective down-sampling process. The classic singular value thresholding (SVT) is modified into a global iteration to solve the low-rank minimization problem. Extensive...
In partial duplicate image retrieval systems, min-Hash algorithms are widely used because of its high efficiency and robustness. In most of min-Hash algorithms, min-Hash functions are considered independent and grouped into tuples called sketches, the discriminative power of sketches are limited. By modeling correlations of min-Hash functions, we propose a novel sketch construction method called Nonpara-metric...
Visual saliency plays an important role in various video applications such as video retargeting and intelligent video advertising. However, existing visual saliency estimation approaches often construct a unified model for all scenes, thus leading to poor performance for the scenes with diversified contents. To solve this problem, we propose a multi-task rank learning approach which can be used to...
Given countless web videos available online, one problem is how to help users find videos to their taste in an efficient way. In this paper, to facilitate userpsilas browsing we propose relevant and exploratory recommendation algorithms utilizing multimodal similarity and contextual network to organize web videos of various topics. Comparison experiments demonstrate proposed approach generates more...
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