Relevance feedback (RF) is a powerful technique in content-based image retrieval (CBIR) system and has become a very active research topic in the past few years. At the early stage of CBIR, research primarily focused on exploring various feature representation and ignored the subjectivity of human perception. There exists a gap between high-level concepts and low-level features. As an effective solution, the RF technique has been used on many CBIR systems to improve the retrieval precision. In this paper, a novel relevance feedback method is proposed to improve the retrieval performance of CBIR. By moving the query vector and updating the weighting factors simultaneously, the convergence speed of the relevance feedback retrieval is accelerated. Experimental results show that this method achieves high accuracy and effectiveness in CBIR.
['Baice Li', 'Senmiao Yuan']
A novel relevance feedback method in content-based image retrieval