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基于内容的图像检索中SVM和Boosting方法集成应用 被引量：3

Ensemble application of SVM and Boosting in content-based image retrieval
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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。 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 resuhs show that the AdaBoostSVM algorithm can improve the performance of retrieval system in the database of 2000 images effectively.
作者 解洪胜 张虹
机构地区 中华女子学院山东分院计算机系 中国矿业大学计算机科学与技术学院
出处 《计算机应用》 CSCD 北大核心 2009年第4期979-981,989,共4页 journal of Computer Applications
基金 国家杰出青年科学基金资助项目(50225414)
关键词 基于内容的图像检索 相关反馈 支持向量机 ADABOOST算法 Content-Based Image Retrieval（CBIR） relevance feedback Support Vector Machine （SVM） Adaboost algorithm
分类号 TP391 [自动化与计算机技术—计算机应用技术]
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参考文献11

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共引文献170

同被引文献18

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计算机应用
2009年 第4期

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