@inproceedings{f70c2d3c558a48e7a5cb28dbd361f92d,
title = "Unsupervised deep hashing for large-scale visual search",
abstract = "Learning based hashing plays a pivotal role in large-scale visual search. However, most existing hashing algorithms tend to learn shallow models that do not seek representative binary codes. In this paper, we propose a novel hashing approach based on unsupervised deep learning to hierarchically transform features into hash codes. Within the heterogeneous deep hashing framework, the autoencoder layers with specific constraints are considered to model the nonlinear mapping between features and binary codes. Then, a Restricted Boltzmann Machine (RBM) layer with constraints is utilized to reduce the dimension in the hamming space. The experiments on the problem of visual search demonstrate the competitiveness of our proposed approach compared to the state of the art.",
keywords = "Autoencoder, Deep learning, Learning based hashing, RBM, Unsupervised learning",
author = "Zhaoqiang Xia and Xiaoyi Feng and Jinye Peng and Abdenour Hadid",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016 ; Conference date: 12-12-2016 Through 15-12-2016",
year = "2017",
month = jan,
day = "17",
doi = "10.1109/IPTA.2016.7821007",
language = "英语",
series = "2016 6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Matti Pietikainen and Abdenour Hadid and Lopez, {Miguel Bordallo}",
booktitle = "2016 6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016",
}