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工程科学与技术:2016,48(5):135-142
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基于最佳路径森林分类的贪婪学习方法在CBIR系统的应用
(1.周口师范学院 网络工程学院,西北大学 可视化研究所;2.西北大学 可视化研究所)
Application of Greedy Learning Based on Optimum path Forest Classification in CBIR System
(1.College of Network Eng.,Zhoukou Normal Univ.,Visualization Inst.,Northwestern Univ.;2.Visualization Inst.,Northwestern Univ.)
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投稿时间:2015-09-14    修订日期:2016-07-20
中文摘要: 针对一般相关反馈的基于内容图像检索(CBIR)方法不能有效处理相关图像和非相关图像的问题,提出了一种基于最佳路径森林分类(OPF)的贪婪学习方法(GL OPF),该方法旨在返回每次迭代查询的最相关图像。首先,查询图像和数据集图像通过Gabor小波变换提取特征向量;然后,通过GL OPF主动学习方法获得图像关联性反馈,生成标记训练集;最后,标记训练集通过OPF分类器进一步评估形成相关性和非相关性原型集,每次迭代都会返回查询的最相关图像。3个公开图像数据集Caltch101、Corel和Pascal上的实验验证了本文方法的有效性。实验结果表明,在3个数据集中,迭代8次时,GL OPF的查询精度比其他3种方法均有较大提高,此外,GL OPF的迭代运行时间和查询时间与OPF几乎相同,很大程度改进了OPF方法。
Abstract:In order to deal with related images and non related images effectively in content based image retrieval (CBIR),a method of greedy learning based on optimum path forest classification(OPF),named as GL OPF,was proposed.Firstly,feature vectors of query images and the images in database were extracted by Gabor wavelet transform.Then,the relevance feedback of images was obtained by GL OPF active learning,generating training set of tags.Finally,prototype sets of relevance and unrelated were formed by further evaluation of OPF classifier of mark sets,and the most relevant query images would return after every iteration.The effectiveness of proposed method was verified by experiments on the three image databases Caltch101,Corel and Pascal.The experimental results showed that in eight iterations,the query precision of GL OPF rises more than that of other three methods.In addition,the running and query time of GL OPF is almost the same as that of OPF.
文章编号:201500958     中图分类号:    文献标志码:
基金项目:国家重点基础研究发展计划前期研究专项资助(2011CB311802);河南省科技厅科技发展计划科技攻关项目资助(122400450356);河南省科技厅科技发展计划软科学项目资助(132400410927)
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孙挺,耿国华.基于最佳路径森林分类的贪婪学习方法在CBIR系统的应用[J].工程科学与技术,2016,48(5):135-142.
Sun Ting,Geng Guohua.Application of Greedy Learning Based on Optimum path Forest Classification in CBIR System[J].Advanced Engineering Sciences,2016,48(5):135-142.