Discriminative Deep Hashing for Scalable Face Image Retrieval

Discriminative Deep Hashing for Scalable Face Image Retrieval

Jie Lin, Zechao Li, Jinhui Tang

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 2266-2272. https://doi.org/10.24963/ijcai.2017/315

With the explosive growth of images containing faces, scalable face image retrieval has attracted increasing attention. Due to the amazing effectiveness, deep hashing has become a popular hashing method recently. In this work, we propose a new Discriminative Deep Hashing (DDH) network to learn discriminative and compact hash codes for large-scale face image retrieval. The proposed network incorporates the end-to-end learning, the divide-and-encode module and the desired discrete code learning into a unified framework. Specifically, a network with a stack of convolution-pooling layers is proposed to extract multi-scale and robust features by merging the outputs of the third max pooling layer and the fourth convolutional layer. To reduce the redundancy among hash codes and the network parameters simultaneously, a divide-and-encode module to generate compact hash codes. Moreover, a loss function is introduced to minimize the prediction errors of the learned hash codes, which can lead to discriminative hash codes. Extensive experiments on two datasets demonstrate that the proposed method achieves superior performance compared with some state-of-the-art hashing methods.
Keywords:
Machine Learning: Classification
Machine Learning: Machine Learning
Machine Learning: Neural Networks
Machine Learning: Deep Learning