Hash Function Learning via Codewords
Abstract
In this paper we introduce a novel hash learning framework that has two main distinguishing features, when compared to past approaches. First, it utilizes codewords in the Hamming space as ancillary means to accomplish its hash learning task. These codewords, which are inferred from the data, attempt to capture similarity aspects of the data's hash codes. Secondly and more importantly, the same framework is capable of addressing supervised, unsupervised and, even, semi-supervised hash learning tasks in a natural manner. A series of comparative experiments focused on content-based image retrieval highlights its performance advantages.
Cite
Text
Huang et al. "Hash Function Learning via Codewords." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2015. doi:10.1007/978-3-319-23528-8_41Markdown
[Huang et al. "Hash Function Learning via Codewords." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2015.](https://mlanthology.org/ecmlpkdd/2015/huang2015ecmlpkdd-hash/) doi:10.1007/978-3-319-23528-8_41BibTeX
@inproceedings{huang2015ecmlpkdd-hash,
title = {{Hash Function Learning via Codewords}},
author = {Huang, Yinjie and Georgiopoulos, Michael and Anagnostopoulos, Georgios C.},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2015},
pages = {659-674},
doi = {10.1007/978-3-319-23528-8_41},
url = {https://mlanthology.org/ecmlpkdd/2015/huang2015ecmlpkdd-hash/}
}