@inproceedings{d12a123edd064badac06057ba3c6da8e,
title = "Deep supervised hashing with information loss",
abstract = "Recently, deep neural networks based hashing methods have greatly improved the image retrieval performance by simultaneously learning feature representations and binary hash functions. Most deep hashing methods utilize supervision information from semantic labels to preserve the distance similarity within local structures, however, the global distribution is ignored. We propose a novel deep supervised hashing method which aims to minimize the information loss during low-dimensional embedding process. More specifically, we use Kullback-Leibler divergences to constrain the compact codes having a similar distribution with the original images. Experimental results have shown that our method outperforms current stat-of-the-art methods on benchmark datasets.",
keywords = "Hashing, Image retrieval, KL divergence",
author = "Xueni Zhang and Lei Zhou and Xiao Bai and Edwin Hancock",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; Joint IAPR International Workshops on Structural and Syntactic Pattern Recognition, SSPR 2018 and Statistical Techniques in Pattern Recognition, SPR 2018 ; Conference date: 17-08-2018 Through 19-08-2018",
year = "2018",
doi = "10.1007/978-3-319-97785-0\_38",
language = "英语",
isbn = "9783319977843",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "395--405",
editor = "Hancock, \{Edwin R.\} and Ho, \{Tin Kam\} and Battista Biggio and Wilson, \{Richard C.\} and Antonio Robles-Kelly and Xiao Bai",
booktitle = "Structural, Syntactic, and Statistical Pattern Recognition - Joint IAPR International Workshop, S+SSPR 2018, Proceedings",
address = "德国",
}