Tang Jinhui, Lin Jie, Li Zechao, Yang Jian
IEEE Trans Neural Netw Learn Syst. 2018 Dec;29(12):6154-6162. doi: 10.1109/TNNLS.2018.2816743. Epub 2018 May 3.
This paper proposes a new discriminative deep quantization hashing (DDQH) approach for large-scale face image retrieval by learning discriminative and compact binary codes. It jointly explores the discrete code learning, batch normalization quantization (BNQ) module, and end-to-end learning in one unified framework, which can guarantee the optimal compatibility of hash coding and feature learning. To learn multiscale and robust facial features, a deep network properly stacking several convolution-pooling layers and pooling layers is designed, and the facial features are obtained by fusing the outputs of the last convolutional layer and the last pooling layer. Besides, the prediction errors of the learned binary codes are minimized to learn discriminative binary codes of images. To obtain higher retrieval accuracies, a BNQ module is utilized to control quantization at a moderate level. Experiments are conducted on two widely used data sets, and the proposed DDQH method achieves encouraging improvements over some state-of-the-art hashing approaches.
本文提出了一种新的判别式深度量化哈希（DDQH）方法，用于通过学习判别性和紧凑的二进制代码进行大规模人脸图像检索。它在一个统一的框架中联合探索离散代码学习、批量归一化量化（BNQ）模块和端到端学习，这可以保证哈希编码和特征学习的最佳兼容性。为了学习多尺度和鲁棒的面部特征，设计了一个适当堆叠几个卷积池化层和池化层的深度网络，并通过融合最后一个卷积层和最后一个池化层的输出获得面部特征。此外，将学习到的二进制代码的预测误差最小化，以学习图像的判别性二进制代码。为了获得更高的检索准确率，利用BNQ模块将量化控制在适度水平。在两个广泛使用的数据集上进行了实验，所提出的DDQH方法相对于一些现有的哈希方法取得了令人鼓舞的改进。
