基于伪成对标签的深度无监督哈希学习

Deep Unsupervised Hashing with Pseudo Pairwise Labels

无监督的深度哈希学习方法由于缺少相似性监督信息,难以获取高质量的哈希编码.因此,文中提出端到端的基于伪成对标签的深度无监督哈希学习模型.首先对由预训练的深度卷积神经网络得到的图像特征进行统计分析,用于构造数据的语义相似性标签.再进行基于成对标签的有监督哈希学习.在两个常用的图像数据集CIFAR-10、NUS-WIDE上的实验表明,经文中方法得到的哈希编码在图像检索上的性能较优.

It is difficult to obtain high-quality hash codes for unsupervised deep hashing methods due to the lack of similarity supervised information.Therefore,an end-to-end deep unsupervised hashing model based on pseudo-pairwise labels is proposed.Statistical analysis is performed on the image features extracted by the pre-trained deep convolutional neural network to construct the semantic similarity labels for data.Supervised deep hashing based on pairwise labels is then conducted.Experiments on commonly used image datasets CIFAR-10 and NUS-WIDE indicate that hash codes obtained by the proposed method perform better on image retrieval.