Abstract: An AdaBoostSVM (AdaBoost Support Vector Machine) algorithm applied to content-based image retrieval was proposed. It uses Support Vector Machine (SVM) as component classifier of the AdaBoost algorithm, and simulates the basic sample re-weighting method of AdaBoost algorithm by adding important samples based on relevance feedback mechanism. The experimental results show that the AdaBoostSVM algorithm can improve the performance of retrieval system in the database of 2000 images effectively.

Key words: Content-Based Image Retrieval (CBIR), relevance feedback, Support Vector Machine (SVM), Adaboost algorithm

摘要： 提出一种适用于图像内容检索的AdaBoostSVM算法。算法思想是采用支持向量机(SVM)作为AdaBoost算法的分量分类器；基于相关反馈检索机制，通过增加重要样本来模拟AdaBoost算法的权重调整方法。在包含2000幅图像的数据库中进行了检索实验，结果表明AdaBoostSVM算法能有效提高系统的检索性能。

关键词: 基于内容的图像检索, 相关反馈, 支持向量机, AdaBoost算法