Image retrieval on the social media platforms propels the tourism information sharing on the Internet. Existing image retrieval methods lack the capability of discovering global statistical distribution of feature representations at multiple scales. In this article, we propose a gated multiscale NetVLAD network, which constructs feature pyramid network based on ResNet backbone and computes NetVLAD features at each pyramid level to capture the multiscale information. In addition, we use gate mechanism for each level of the pyramid to adaptively represent the contribution of each level of features to the retrieval task. Experimental results on CIFAR-10, MNIST, and the Google street view datasets show that our image retrieval method has achieved better results than several existed methods.

中文翻译：

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通过门控多尺度NetVLAD进行社交媒体应用的图像检索

社交媒体平台上的图像检索推动了互联网上的旅游信息共享。现有的图像检索方法缺乏在多个尺度上发现特征表示的全局统计分布的能力。在本文中，我们提出了一种门控多尺度NetVLAD网络，该网络基于ResNet主干构造要素金字塔网络，并在每个金字塔级别计算NetVLAD特征以捕获多尺度信息。此外，我们对金字塔的每个级别使用门机制，以自适应地表示每个级别的要素对检索任务的贡献。在CIFAR-10，MNIST和Google街景数据集上的实验结果表明，我们的图像检索方法比几种现有方法取得了更好的结果。