Enhancing SVM Active Learning for Image Retrieval Using Semi-supervised Bias-Ensemble

Support vector machine (SVM) based active learning technique plays a key role to alleviate the burden of labeling in relevance feedback. However, most SVM-based active learning algorithms are challenged by the small example problem and the asymmetric distribution problem. This paper proposes a novel active learning scheme that deals with SVM ensemble under the semi-supervised setting to address the fist problem. For the second problem, a bias-ensemble mechanism is developed to guide the classification model to pay more attention on the positive examples than the negative ones. An empirical study shows that the proposed scheme is significantly more effective than some existing approaches.