Integrating unlabeled images for image retrieval based on relevance feedback
Retrieval techniques based on pure similarity metrics are often suffered from the scales of image features. An alternative approach is to learn a mapping based on queries and relevance feedback by supervised learning. However, the learning is plagued by the insufficiency of labeled training images. Different from most current research in image retrieval, this paper investigates the possibility of taking advantage of unlabeled images in the given image database to make a hybrid statistical learning feasible. Assuming a generative model of the database, the proposed approach casts image retrieval as a transductive learning problem in a probabilistic framework. Our experiments show that the proposed approach has a satisfactory performance in image retrieval applications.