Active learning [3, 6, 8] is a promising tool to improve the performance of Content-Based Image Retrieval (CBIR). As a commonly used active learning approach, Angle-diversity [3, 5, 6] provides the most informative images to user for feedback [10]. However, it suffers from the problem that the query concept is diverse and the numbers of the positive and the negative images are imbalanced. As a consequence, the positive samples obtained by active learning are inadequate, which degrades the learning efficiency. To deal with this issue, we propose a novel method based on angle-diversity and hyperplane shifting to increase the number of positive images in the active learning results. The experiment is conducted on a test data set with 10,000 images. Compared with the traditional Angle-diversity technique, our method can improve the retrieval performance significantly.
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