Exploiting unlabeled data in content-based image retrieval

Abstract

In this paper, the SSAIR (Semi-Supervised Active Image Retrieval) approach, which attempts to exploit unlabeled data to improve the performance of content-based image retrieval (CBIR), is proposed. This approach combines the merits of semi-supervised learning and active learning. In detail, in each round of relevance feedback, two simple learners are trained from the labeled data, i.e. images from user query and user feedback. Each learner then classifies the unlabeled images in the database and passes the most relevant/irrelevant images to the other learner. After re-training with the additional labeled data, the learners classify the images in the database again and then their classifications are merged. Images judged to be relevant with high confidence are returned as the retrieval result, while these judged with low confidence are put into the pool which is used in the next round of relevance feedback. Experiments show that semi-supervised learning and active learning mechanisms are both beneficial to CBIR. © Springer-Verlag Berlin Heidelberg 2004.