文章基本信息

标题：Toward Improving Image Retrieval via Global Saliency Weighted Feature
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作者：Hongwei Zhao ; Jiaxin Wu ; Danyang Zhang 等
期刊名称：ISPRS International Journal of Geo-Information
电子版ISSN：2220-9964
出版年度：2021
卷号：10
期号：4
页码：249
DOI：10.3390/ijgi10040249
语种：English
出版社：MDPI AG
摘要：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.