For full description of images’ semantic information, image retrieval tasks are increasingly using deep convolution features trained by neural networks. However, to form a compact feature representation, the obtained convolutional features must be further aggregated in image retrieval. The quality of aggregation affects retrieval performance. In order to obtain better image descriptors for image retrieval, we propose two modules in our method. The first module is named generalized regional maximum activation of convolutions (GR-MAC), which pays more attention to global information at multiple scales. The second module is called saliency joint weighting, which uses nonparametric saliency weighting and channel weighting to focus feature maps more on the salient region without discarding overall information. Finally, we fuse the two modules to obtain more representative image feature descriptors that not only consider the global information of the feature map but also highlight the salient region. We conducted experiments on multiple widely used retrieval data sets such as roxford5k to verify the effectiveness of our method. The experimental results prove that our method is more accurate than the state-of-the-art methods.

中文翻译：

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通过全局显着性加权功能改善图像检索

为了完整描述图像的语义信息，图像检索任务越来越多地使用由神经网络训练的深度卷积特征。但是，为了形成紧凑的特征表示，必须在图像检索中进一步聚合获得的卷积特征。聚合的质量会影响检索性能。为了获得更好的图像描述符用于图像检索，我们在方法中提出了两个模块。第一个模块被称为卷积的广义区域最大激活（GR-MAC），它更加关注多尺度的全局信息。第二个模块称为显着性联合权重，它使用非参数显着性权重和通道权重将特征图更多地聚焦在显着区域上，而不会丢弃整体信息。最后，我们将这两个模块融合在一起，以获得更具代表性的图像特征描述符，这些描述符不仅考虑特征图的全局信息，而且突出显示显着区域。我们对多种广泛使用的检索数据集（例如roxford5k）进行了实验，以验证我们方法的有效性。实验结果证明，我们的方法比最新方法更准确。