Bag-of-Words Based Deep Neural Network for Image Retrieval
2015-03-20 15:23
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本文提出了基于DNN的BOW 图像检索
问题:
(1)query是什么图像?
1、introduction
数据集:MSR(microsoft second Research)-Bing Grand Challenge.包括11.7million queries、1 million images。数据集从这篇文章得到的Clickage: Towards bridging
semantic and intent gaps via mining click logs of search
engines.
传统的方法:计算比较图像或者文本之间的特征,这些特征是从图像文本或者是底层特征(sift,HOG,LBP)
本文:通过DNN学习高层图像特征。
DNN在图像检索上的应用【1】,这种方法在训练数据庞大的时候是不切实际的。而在本文中,我们提出了一个新DNN算法,可以掌控大规模的queries和直接使用计算image和query的相关度。我们的模型针对两个目的:1、query-image相关性估计,2、基于高层视觉特征估计图图的相似度。
2、基于DNN的bag of words
问题:
(1)query是什么图像?
1、introduction
数据集:MSR(microsoft second Research)-Bing Grand Challenge.包括11.7million queries、1 million images。数据集从这篇文章得到的Clickage: Towards bridging
semantic and intent gaps via mining click logs of search
engines.
传统的方法:计算比较图像或者文本之间的特征,这些特征是从图像文本或者是底层特征(sift,HOG,LBP)
本文:通过DNN学习高层图像特征。
DNN在图像检索上的应用【1】,这种方法在训练数据庞大的时候是不切实际的。而在本文中,我们提出了一个新DNN算法,可以掌控大规模的queries和直接使用计算image和query的相关度。我们的模型针对两个目的:1、query-image相关性估计,2、基于高层视觉特征估计图图的相似度。
2、基于DNN的bag of words
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