Image retrieval based on deep learning of hash has made great progress. The hash method increases retrieval speed greatly while saving storage space. However, some problems exist, such as the distinctiveness of image feature still needs to be improved and some image features are still redundant. We propose a new deep learning to hash method, namely, deep multiscale divergence hashing, which provides a high diversity and compact image feature for image retrieval. The discriminative features from deep neural networks are identified by the importance criterion in network pruning techniques and the feature redundancy is reduced. Then, the selected features across different layers are fused in a certain proportion to increase the diversity of features for image retrieval. We also present a hybrid loss function in hash space, which consists of the weighted pairwise cross-entropy loss function and the KL-divergence. It tends to minimize the hamming distance between similar images and maximize the hamming distance between different images, which helps improve the accuracy. Massive experimental results show that our method achieves better feature distinguishability and more advanced image retrieval accuracy, and surpasses HashNet by 11.46%, 7.58%, and 13.86% on MS COCO, NUS-WIDE, and CIFAR-10 datasets, respectively.

中文翻译：

---

深度多尺度散度散列用于图像检索

基于深度学习哈希的图像检索取得了长足的进步。哈希方法大大提高了检索速度，同时节省了存储空间。但是，存在一些问题，例如图像特征的特殊性仍需要改进，某些图像特征仍然是多余的。我们提出了一种新的深度学习哈希算法，即深度多尺度散度哈希算法，它为图像检索提供了高多样性和紧凑的图像特征。通过神经修剪技术中的重要性标准，可以识别出深度神经网络的可区分特征，从而减少了特征冗余。然后，将跨不同图层的选定特征按一定比例融合，以增加用于图像检索的特征的多样性。我们还提出了哈希空间中的混合损失函数，它由加权的成对交叉熵损失函数和KL散度组成。它倾向于最小化相似图像之间的汉明距离，并最大化不同图像之间的汉明距离，这有助于提高准确性。大量实验结果表明，该方法在MS COCO，NUS-WIDE和CIFAR-10数据集上分别具有更好的特征可分辨性和更高的图像检索精度，分别比HashNet分别高11.46％，7.58％和13.86％。