基于多视角低秩表征的短视频多标签学习模型

Microvideo Multilabel Learning Model Based on Multiview Low-Rank Representation

提出一种基于多视角低秩表征的短视频多标签分类模型。该模型将低秩表征和多标签学习结合到同一框架中,利用不同类型特征的一致性学习本征稳定的低秩表示。同时为了获得标签相关性的潜在表示,构建了标签相关性学习项来自适应地捕获标签的相关性矩阵。此外,模型利用监督信息进一步提高了其表征能力。大量的实验结果证实了所提方法的优越性。

We propose a microvideo multilabel learning model based on a multiview low-rank representation,which combines the low-rank representation and multilabel learning into a unified framework and uses the consistency in different features to learn an intrinsically robust low-rank representation.Meanwhile,to represent the potential label correlations,our proposed model constructs a label correlation learning term to adaptively capture the labels’correlation matrix.Furthermore,the supervised information is exploited to further improve the representation ability of our model.Extensive experiments on a large-scale public dataset show the effectiveness of the proposed scheme.