Image sequence recognition with active learning using uncertainty sampling

In this paper we consider the case when huge datasets need to be labeled efficiently for learning. It is assumed that the data can be naturally organized into many small groups, called chunklets, each one of which contains data from the same class, and many chunklets are available from each class. Each chunklet exhibits some of the typical variation representative for the class. We investigate how active learning methods based on uncertainty sampling perform in this setting, and whether any gains can be expected in comparison with random sampling. We also propose a novel strategy for selecting which chunklets to be selected for labeling. Experiments with 7containing variation in pose, expression and illumination conditions illustrate the proposed method.