To address the semantic gap challenges in the content-based image retrieval, an improved relevance feedback system based on hybrid GA and active learning SVM model was proposed in this text. There are two main improvements for the new system. First, the GA with∕without feature selection are used to optimal the parameters ( C and У ) and sub-features in the SVM classifier. Second, the active SVM was applied on actively selecting most information images that minimizes redundancy between the candidate images shown to the user. The experimental results show the proposed approach has the speedy convergence and good stability in the relevant feedback system.