Active learning with multiple classifiers for multimedia indexing

We propose and evaluate in this paper a combination of Active Learning and Multiple Classifiers approaches for corpus annotation and concept indexing on highly imbalanced datasets. Experiments were conducted using TRECVID 2008 data and protocol with four different types of video shot descriptors, with two types of classifiers (Logistic Regression and Support Vector Machine with RBF kernel) and with two different active learning strategies (relevance and uncertainty sampling). Results show that the Multiple Classifiers approach significantly increases the effectiveness of the Active Learning. On the considered dataset, the best performance is achieved when 15 to 30% of the corpus is annotated for individual descriptors and when 10 to 15% of the corpus is annotated for their fusion.