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Multi-Graph Semi-Supervised Learning for Video Semantic Feature Extraction

This paper proposes a video semantic feature extraction approach based on multi-graph semi-supervised learning, which aims to simultaneously deal with the insufficiency of training data and the curse of dimensionality. In contrast to traditional graph-based semi-supervised learning, which generates graph from high-dimensional low-level features, we separate the original low-level features into multiple modalities with minimum correlations, and thus multiple graphs are obtained from these modalities. This way can tackle the curse of dimensionality brought by the high-dimensional feature space. We then propose a criterion to optimally fuse these graphs based on the pairwise relationships among training samples, and implement semi-supervised learning on the fused graph. Experimental results have demonstrated the effectiveness of the proposed approach.