video-to-shot tag allocation by weighted sparse group lasso
Traditional shot tagging techniques are focused on learning and propagating the tags at the same level, that is from labeled training shots to the unknown test shots. Due to the lack of sufficient labeled video shots, effective shot tagging remains challenging. By observing that video-level tags are more widely provided, we design a novel approach to propagate video-level tags to the test shots. A weighted sparse group lasso method (WSGL) is proposed for shot reconstruction, which well preserves the structural sparsity to reduce the noise in tag propagation. Meanwhile, it simultaneously considers the spatial-temporal information within the video corpus to enhance the tagging performance. Extensive experiments are conducted on two public video datasets to demonstrate the effectiveness of the proposed method.
structure sparsity video tagging