This chapter introduces the application of active learning in video annotation. The insufficiency of training data is a major obstacle in learning-based video annotation. Active learning is a promising approach to dealing with this difficulty. It iteratively annotates a selected set of most informative samples, such that the obtained training set is more effective than that gathered randomly. The authors present a brief review of the typical active learning approaches. They categorize the sample selection strategies in these methods into five criteria, that is, risk reduction, uncertainty, positivity, density, and diversity. In particular, they introduce the Support Vector Machine (SVM)-based active learning scheme which has been widely applied. Afterwards, they analyze the deficiency of the existing active learning methods for video annotation, that is, in most of these methods the to-be-annotated concepts are treated equally without preference and only one modality is applied. To address these two issues, the authors introduce a multi-concept multi-modality active learning scheme. This scheme is able to better explore human labeling effort by considering both the learnabilities of different concepts and the potential of different modalities.
A New Approach for Remote Sensing Image Sample Selection Based on Convex Theory
