Representative Feature Matching Network for Image Retrieval

Recent convolutional neural network (CNNs) have shown promising performance on image retrieval due to the powerful feature extraction capability. However, the potential relations of feature maps are not effectively exploited in the before CNNs, resulting in inaccurate feature representations. To address this issue, we excavate feature channel-wise realtions by a matching strategy to adaptively highlight informative features. In this paper, we propose a novel representative feature matching network (RFMN) for image hashing retrieval. Specifically, we propose a novel representative feature matching block (RFMB) that can match feature maps with their representative one. So, the significance of each feature map can be exploited according to the matching similarity. In addition, we also present an innovative pooling layer based on the representative feature matching to build relations of pooled features with unpooled features, so as to highlight the pooled features retained more valuable information. Extensive experiments show that our approach can promote the average results of conventional residual network more than 2.6% on Cifar-10 and 1.4% on NUS-WIDE dataset, meanwhile achieve the state-of-the-art performance.