att_abstract={{Similarity-based image hashing represents crucial technique for visual data storage reduction and expedited image search. Conventional hashing schemes typically feed hand-crafted features into hash functions, which separates the procedures of feature extraction and hash function learning. In this paper, we propose a novel algorithm that concurrently performs feature engineering and non-linear supervised hashing function learning. Our technical contributions in this paper are two-folds: 1) deep network optimization is often achieved by gradient propagation, which critically requires a smooth objective function. The discrete nature of hash codes makes them not amenable for gradient-based optimization. To address this issue, we propose an exponential hashing loss function and its bilinear smooth approximation. Effective gradient calculation and propagation are thereby enabled; 2) pre-training is an important trick in supervised deep learning. The impact of pre-training on the hash code quality has never been discussed in current deep hashing literature. We propose a pre-training scheme inspired by recent advance in deep network based image classification, and experimentally demonstrate its effectiveness. Comprehensive quantitative evaluations are conducted. On all adopted benchmarks, our proposed algorithm generates new performance records by significant improvement margins.}},
	att_categories={C_P1, C_P2},
	att_copyright_notice={{The definitive version was published in Association for the Advancement of Artificial Intelligence. {{, 2017-02-04}}{{, http://www.aaai.org/Conferences/AAAI/2017/aaai17call.php}}
	att_tags={image signature,  data hashing,  deep learning},
	author={Yadong Mu and Zhu Liu},
	institution={{Association for the Advancement of Artificial Intelligence}},
	title={{Deep Hashing: A Joint Approach for Image Signature Learning}},