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.

摘要： 主动学习已被证明是提升基于内容图像检索性能的一种重要技术。而相关反馈技术可以有效地减少用户标注。提出一种主动学习算法，带权Co-ASVM，用于改进相关反馈中样本选择的性能。颜色和纹理可以认为是一张图片的两个充分不相关的视图，分别计算颜色和纹理两种特征空间的权值，并在两种特征空间上分别进行SVM学习，对未标注样本进行分类；为了减少反馈样本的冗余，提出一种K-means聚类的主动反馈策略，将未标注样本返回给用户标注。实验表明，该图像检索方法有较高的准确性，并且有不错的检索效果。