A semi-supervised learning approach for CBIR systems with relevance feedback

Semi-supervised learning techniques are gaining importance in the scenario of constantly growing data collections. CBIR systems must be able to autonomously analyze the patterns available, to fully exploit unlabeled data with the final objective of identifying an optimal representation space where data belonging to the same semantic class are close to each other. In this work we propose to adopt relevance feedback as a mean of collecting information about the semantic classes perceived by the user and to exploit this information for a long-term learning process where a more effective feature space can be obtained by a proper metric learning technique and class labels can be automatically assigned to unlabeled patterns. The process can iterate as new data become available thus providing a tool for successfully managing new incoming data. The experimental results will confirm the advantages of the proposed learning approach.