Selective Multi-cotraining for Video Concept Detection

Research interest in cotraining is increasing which combines information from usually two classifiers to iteratively increase training resources and strengthen the classifiers. We try to select classifiers for cotraining when more than two representations of the data are available. The classifier based on the selected representation or data descriptor is expected to provide the most complementary information as new labels for the target classifier. These labels are critical for the next learning iteration. We present two criteria to select the complementary classifier where classification results on a validation set are used to calculate statistics for all the available classifiers. These statistics are used not only to pick the best classifier but also ascertain the number of new labels to be added for the target classifier. We demonstrate the effectiveness of classifier selection for semantic indexing task on the TRECIVD 2013 dataset and compare it to the self-training.