Enhancing relevance feedback in image retrieval using unlabeled data

Relevance feedback is an effective scheme bridging the gap between high-level semantics and low-level features in content-based image retrieval (CBIR). In contrast to previous methods which rely on labeled images provided by the user, this article attempts to enhance the performance of relevance feedback by exploiting unlabeled images existing in the database. Concretely, this article integrates the merits of semisupervised learning and active learning into the relevance feedback process. In detail, in each round of relevance feedback two simple learners are trained from the labeled data, that is, images from user query and user feedback. Each learner then labels some unlabeled images in the database for the other learner. After retraining with the additional labeled data, the learners reclassify the images in the database and then their classifications are merged. Images judged to be positive with high confidence are returned as the retrieval result, while those judged with low confidence are put into the pool which is used in the next round of relevance feedback. Experiments show that using semisupervised learning and active learning simultaneously in CBIR is beneficial, and the proposed method achieves better performance than some existing methods.