中文摘要：

为快速提高相关反馈算法的效率，提出一种记忆与半监督相结合的主动相关反馈算法．在检索初期，利用记忆信息获得较多的正训练样本，利用用户已标记样本与数据库内未标记样本有效地解决训练样本不平衡问题，获得准确的初始SVM分类器；在检索后期，利用主动学习算法寻找数据库内对优化学习过程中最有用的样本请求用户标记，减少用户标记的样本量，加快收敛速度．对5000幅Corel图像数据库的实验表明，与传统相关反馈算法相比，新算法能够显著提高学习器的效率和性能，并快速收敛于用户的查询概念．

英文摘要：

To improve the efficiency of relevance feedback quickly, an integrated memorization and semi-supervision active relevance feedback algorithm is presented. In its early stage, more positive samples are obtained through memorization. The problem of biased training samples is solved efficiently through labeled and unlabeled training samples and accurate initial SVM classifier is obtained; in the later stage, samples required for labeling by users reduced largely and convergent rate improved greatly by the active learning algorithm which selects the most useful samples in database to solicit the user for labeling. Experimental results on 5 000 Corel images library showed that the proposed algorithm can greatly improve the efficiency and accuracy and converge to user＇s query concept quickly.