Improving video concept detection through label space partitioning

We present an approach to video concept detection by building binary trees partitioning the label space, using visual and semantic similarity for multi-label datasets. The technique overcomes sparse annotations problem by increasing the number of positive examples per concept with the number of classifiers per concept, though sub-optimal, augmented too. We draw similarities between the proposed tree generation approach and Error Correcting Output Codes (ECOC) for multi-label classification and build ranked lists of video shots using weighted decoding or weighted tree traversal. We build a set of different trees based on the presented criterion each partitioning the label space in its own specific way. Inspired by the work in [1] we amass information from ensemble of trees to build the final ranked list, but using a different criterion. The classification resulting in ensemble error correction is complementary to One-vs-All classification and increases concept detection performance significantly on the TRECVID 2010 and 2013 datasets.