Infrequent concept pairs detection in multimedia documents
Abdelkader Hamadi, Philippe Mulhem, Georges Quénot · 2014
Single visual concept detection in videos is a hard task, especially for infrequent concepts or for those difficult to model. This question becomes even more difficult in the case of concept pairs. Two main directions may tackle this problem: 1) combine the predictions of their corresponding detectors in a way which is similar to usual information retrieval, or 2) build supervised learners for these pairs of concepts by generating annotations based on the occurrences of the two individual concepts. Each of these approaches have advantages and drawbacks. We evaluated them in the context of the concept pair detection subtask of the TRECVid 2013 semantic indexing (SIN) task and found that information retrieval-like fusions of concept detection scores outperforms the learning approaches. The described methods outperform the best official result of the evaluation campaign cited previously, by 9% in terms of relative improvement on MAP.