Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 29, Issue 12, Pages 6154-6162Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2018.2816743
Keywords
Batch normalization quantization (BNQ); end-to-end learning; hashing; image retrieval
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Funding
- 973 Program of China [2014CB347600]
- National Natural Science Foundation of China [61522203, 61772275, 61732007]
- Natural Science Foundation of Jiangsu Province [BK20170033]
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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.
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