Relevance feedback algorithm based on learning from labeled and unlabeled data

Supervised learning algorithms (relevance feedback (RF) algorithms) are often used in content based image retrieval (CBIR) systems to enhance interactive search and browsing of image databases. One of the issues associated with RF based CBIR systems is the lack of a large training set. Labeling of images is a time consuming activity and user´s usually do not have the patience to label a large set. The challenge is to somehow leverage the much larger set of unlabeled images to improve the performance of CBIR systems. In this paper we propose a novel RF algorithm which learns from both labeled and unlabeled data. Our proposed algorithm also uses active learning so as to maximize the information gained from a given amount of user feedback.