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用于提高谷歌图像搜索结果的二分类器在线学习方法

万玉钗 刘峡壁 韩菲霏 童坤琦 刘宇

万玉钗, 刘峡壁, 韩菲霏, 童坤琦, 刘宇. 用于提高谷歌图像搜索结果的二分类器在线学习方法. 自动化学报, 2014, 40(8): 1699-1708. doi: 10.1004/SP.J.1004.2014.01699
引用本文: 万玉钗, 刘峡壁, 韩菲霏, 童坤琦, 刘宇. 用于提高谷歌图像搜索结果的二分类器在线学习方法. 自动化学报, 2014, 40(8): 1699-1708. doi: 10.1004/SP.J.1004.2014.01699
WAN Yu-Chai, LIU Xia-Bi, HAN Fei-Fei, TONG Kun-Qi, LIU Yu. Online Learning a Binary Classifier for Improving Google Image Search Results. ACTA AUTOMATICA SINICA, 2014, 40(8): 1699-1708. doi: 10.1004/SP.J.1004.2014.01699
Citation: WAN Yu-Chai, LIU Xia-Bi, HAN Fei-Fei, TONG Kun-Qi, LIU Yu. Online Learning a Binary Classifier for Improving Google Image Search Results. ACTA AUTOMATICA SINICA, 2014, 40(8): 1699-1708. doi: 10.1004/SP.J.1004.2014.01699

用于提高谷歌图像搜索结果的二分类器在线学习方法

doi: 10.1004/SP.J.1004.2014.01699

Online Learning a Binary Classifier for Improving Google Image Search Results

Funds: 

Supported by National Natural Science Foundation of China (60973059, 81171407) and Program for New Century Excellent Tal-ents in University of China (NCET-10-0044)

  • 摘要: 对于基于关键词的图像检索,利用检索结果的视觉相似性学习二分类器有望成为改善检索结果的最有效途径之一. 为改善搜索引擎的搜索结果,本文提出一种算法框架并且基于此框架着重研究训练数据选择这一关键问题. 训练数据选择过程由两个阶段组成:1)训练数据初始化以开始分类器学习过程;2)分类器迭代学习过程中的动态数据选择. 对于初始训练数据的选择,我们探讨了基于聚类和基于排序两种方法,并且对比了自动训练数据选择与人工标注的结果. 对于动态数据选择,我们比较了支持向量机和基于最大最小后验伪概率的贝叶斯分类器的分类效果. 组合上述两个阶段的不同方法,我们得到了8种不同的算法,并将其用于谷歌搜索引擎进行基于关键词的图像检索. 实验结果证明,如何从含有噪声的搜索结果中选择训练数据是搜索结果改善的关键问题. 实验显示我们的方法能够有效的改善谷歌搜索的结果,尤其是排序在前的结果. 尽早为用户提供更相关的结果能够更大程度的减少用户逐个翻页查看结果的工作. 另外,如何使自动训练数据选择与人工标注媲美仍是需要继续研究的一个问题.
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出版历程
  • 收稿日期:  2012-09-24
  • 修回日期:  2013-12-11
  • 刊出日期:  2014-08-20

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