Automatic video tagging using content redundancy
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Label to region by bi-layer sparsity priors
MM '09 Proceedings of the 17th ACM international conference on Multimedia
On the Annotation of Web Videos by Efficient Near-Duplicate Search
IEEE Transactions on Multimedia
Effective transfer tagging from image to video
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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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.