基于四元组完备损失的智能图像检索方法

An image retriceval method based on quadruplet complete loss

在图像检索领域,将三元组排序损失应用于深度神经网络权重的更新,可以使生成的图像表示保存更多的语义特征,但是三元组排序损失没有全面的考虑不同类别图像之间的关联。为此提出了一种四元组完备损失,此损失函数将图像类间相似性小于类内相似性的特点融入到损失函数的构建中。与三元组排序损失函数相比,此函数可以更全面地体现查询图像与同类和不同图像之间的相似性关系。进一步,本文还提出了一种有效的基于四元组的深度网络结构,可用于图像的哈希检索。实验结果表明,提出的方法能够在CIFAR-10、SVHN和NUS-WIDE图像库中取得良好的检索性能。

Tuning the weights of deep neural networks using loss and back propagation algorithm has been widely used in image retrieval. Applying triplet ranking loss to tune the weights can make the generated image representations preserve more semantic features. However, the relations among different categories of images are not fully considered in the triplet ranking loss.The quadruplet complete loss is proposed based on that inter-class similarity is smaller than the intra-class similarity, and the similarities among the query image and similar or dissimilar images are also fully considered in the loss.Further more, an effective quadruplet based deep hashing network architecture is also proposed for image retrieval. The experimental results show that our method can achieve excellent retrieval performance in CIFAR-10, SVHN and NUS-WIDE.