面向图像检索的深度汉明嵌入哈希

Deep Hamming Embedding Based Hashing for Image Retrieval

深度卷积神经网络学习的图像特征表示具有明显的层次结构.随着层数加深,学习的特征逐渐抽象,类的判别性也逐渐增强.基于此特点,文中提出面向图像检索的深度汉明嵌入哈希编码方式.在深度卷积神经网络的末端插入一层隐藏层,依据每个单元的激活情况获得图像的哈希编码.同时根据哈希编码本身的特征提出汉明嵌入损失,更好地保留原数据之间的相似性.在CIFAR-10、NUS-WIDE基准图像数据集上的实验表明,文中方法可以提升图像检索性能,较好改善短编码下的检索性能.

The image features learned by deep convolutional neural networks have an obvious hierarchical structure. As the number of layers deepens, the learned features become more and more abstract and the discrimination of classes is gradually enhanced. Based on the above, deep hamming embedding based hashing for image retrieval is proposed. A hidden layer is inserted at the end of the deep convolutional neural network and then hash codes are obtained by the activation of each unit of the layer. According to the characteristics of hash codes, hamming embedding loss is proposed to preserve the similarity between the original data better. Experiments on commonly used benchmark image datasets CIFAR-10 and NUS-WIDE indicate that the proposed model improves image retrieval performance and performs better with short encoding length.