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基于最大间隔的半监督图像搜索重排序方法

A Max Margin Based Semi-Supervised Reranking Method

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摘要

提出一种基于最大间隔原理的半监督图像搜索重排序学习算法。所提算法在最大间隔原理框架下,首先利用超图正则化保持标注及未标注样本在原始空间中的局部近邻关系,增强算法的稳健性;其次,利用少量的标注样本构造优先关系对,将样本间先验的相关性等级信息引入目标函数中以更好地指导重排序模型的学习。在公开数据集MSRA-MM 1.0上的实验结果表明所提方法能更好地将符合用户需求的结果靠前优先呈现给用户,提高搜索的准确性。

Abstract

We propose a max margin based semi-supervised reranking method for multimedia information retrieval. We use hypergraph regularization to preserve the neighborhood of the sample in the original space and introduce the labeled and unlabeled sample information to construct the objective function, so as to achieve full and efficient use of data information for ranking. By using a small amount of annotation samples to construct the priority relationship pairs, the priority information between samples is introduced into the objective function to construct a ranking learning model. This method can show users in priority the results that meet their demand better, and improve the retrieval accuracy. The experimental results on MSRA-MM 1.0 dataset suggest the proposed method provides superior performance compared with several state-of-the-art methods.

Newport宣传-MKS新实验室计划
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中图分类号:TP391

DOI:10.3788/LOP55.111001

所属栏目:图像处理

基金项目:国家自然科学基金(61271069)

收稿日期:2018-03-23

修改稿日期:2018-05-02

网络出版日期:2018-05-28

作者单位    点击查看

张桐喆:天津大学电气自动化与信息工程学院, 天津 300072
苏育挺:天津大学电气自动化与信息工程学院, 天津 300072
郭洪斌:天津大学电气自动化与信息工程学院, 天津 300072

联系人作者:郭洪斌(ghb3011204117@163.com)

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引用该论文

Zhang Tongzhe,Su Yuting,Guo Hongbin. A Max Margin Based Semi-Supervised Reranking Method[J]. Laser & Optoelectronics Progress, 2018, 55(11): 111001

张桐喆,苏育挺,郭洪斌. 基于最大间隔的半监督图像搜索重排序方法[J]. 激光与光电子学进展, 2018, 55(11): 111001

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