Abstract: Active learning has been proved to be a key technique for improving Content-Based Image Retrieval（CBIR） performance.Relevance feedback technique can effectively reduce the cost of labeling.An active learning algorithm is put forward，weighted Co-Active SVM，to improve the performance of selective sampling in image retrieval.Color and texture are naturally considered as sufficient and uncorrelated views of an image；calculate the weight of color and texture feature space separately.SVM classifiers are learned in color and texture feature subspaces，respectively and the unlabeled data are classified.In order to reduce redundancy between these examples，K-means based active selection criterion is proposed to select images for user’s feedback.The experimental results show that the proposed algorithm has a higher accuracy，and has the better retrieval effect.