Image Retrieval using Long Term Learning Relevance Feedback

Relevance feedback which is used in content-based image retrieval (CBIR) has been considered as the efficient technique to improve the retrieval performances. The traditional relevance feedback technique demonstrates a disability to use the users´ historical feedback information sufficiently gotten together by the system in the former retrieval processes when initiating a new query session. In this paper, an approach to relevance feedback based on long-term learning strategy using the historical retrieval information is presented for the content-based image similarity retrieval. The approach adopts a semantic covering set constructed dynamically to deposit the users´ historical retrieval information produced in previous retrieval processes, and predicts the semantic correlation between the images in database and query sample according to the historical retrieval information when carrying out a new query session. The performance of an experimental image retrieval system using this approach is evaluated on a database of around 3000 images. Empirical results demonstrate improved performances compared with the CBIR system with the traditional relevance feedback technique using the same image similarity measure.