Annotating retrieval database with active learning
In this paper, we describe a retrieval system that uses hidden annotation to improve the performance. The contribution of this paper is a novel active learning framework that can improve the annotation efficiency. For each object in the database, we maintain a list of probabilities, each indicating the probability of this object having one of the attributes. This list of probabilities serves as the basis of our active learning algorithm, as well as semantic features to determine the similarity between objects in the database. We show active learning has better performance than random sampling in all our experiments.