@inproceedings{63af39ab50c249e9a829cd5a8897c9bb,
title = "Large-margin supervised hashing",
abstract = "Learning to hash embeds objects (e.g. images/documents) into a binary space with the semantic similarities preserved from the original space, which definitely benefits large-scale tough tasks such as image retrieval. By leveraging semantic labels, supervised hashing methods usually achieve better performance than unsupervised ones in real-world scenarios. However, most existing supervised methods do not sufficiently encourage inter-class separability and intra-class compactness which is quite crucial in discriminative hashcodes. In this paper, we propose a novel hashing method called Large-Margin Supervised Hashing (LMSH) based on a non-linear classification framework. Specifically, LMSH introduces the angular decision margin which could adjust inter-class separability and intra-class compactness through a hyper-parameter for more discriminative codes. Extensive experiments on three public datasets are conducted to demonstrate the LMSH{\textquoteright}s superior performance to some state-of-the-arts in image retrieval tasks.",
keywords = "Large-margin, Non-linear classification framework, Supervised hashing",
author = "Xiaopeng Zhang and Hui Zhang and Yong Chen and Xianglong Liu",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 24th International Conference on Neural Information Processing, ICONIP 2017 ; Conference date: 14-11-2017 Through 18-11-2017",
year = "2017",
doi = "10.1007/978-3-319-70087-8\_28",
language = "英语",
isbn = "9783319700861",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "259--269",
editor = "Yuanqing Li and Derong Liu and Shengli Xie and El-Alfy, \{El-Sayed M.\} and Dongbin Zhao",
booktitle = "Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings",
address = "德国",
}