图像检索中基于标记与未标记样本的主动学习算法  

Active Learning with Labeled and Unlabeled Samples for Content-Based Image Retrieval

作　　者：徐杰[1] 施鹏飞[1] 

机构地区：[1]上海交通大学图像处理与模式识别研究所,上海200030

出　　处：《上海交通大学学报》2004年第12期2068-2072,共5页Journal of Shanghai Jiaotong University

基　　金：国家自然科学基金资助项目(69931010)

摘　　要：为解决监督学习过程中训练样本集过小问题,提出一种未标记样本辅助学习与主动学习相结合的学习方法.基于用户已标记样本与数据库内未标记样本,利用EM算法得到初始朴素贝叶斯分类器与初始假设;主动学习算法寻找数据库内对于优化学习过程最有用的样本请求用户标记;算法根据用户反馈,迭代更新已有查询概念,直至用户满意为止.实验表明,本文算法能够显著提高学习器的效率和性能,并可快速收敛于用户定义的查询概念.This paper presented an integrated method for unlabeled data assisted active learning in order to solve insufficient labeled training data in supervised learning. The proposed approach uses naive Bayes with expectation maximization (EM) to learn an initial query concept based on the labeled and formerly unlabeled images. Then the active learning algorithm selects the most useful images in database to query the user for labeling, and the query concept is updated iteratively according to user's feedbacks. Various experiments demonstrate that the proposed approach can improve the efficiency and accuracy of the learner and can grasp user's query concept quickly.

关 键 词：基于内容的图像检索 主动学习 朴素贝叶斯分类器 相关反馈 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]