This paper presents a new learning technique for the similarity model refinement in CBIR systems. We propose a whole retrieval strategy based on a new relevance feedback scheme and on a long-term similarity learning algorithm which uses feedback information of previous sessions. We introduce this technique as the simple evolution of the short-term relevance feedback approach into a long-term similarity learning, without additional need of user interaction. Our algorithm is validated via a quality assessment realized on a heterogeneous database of 1,200 color images.