Bank of Quantization Models: A Data-Specific Approach to Learning Binary Codes for Large-Scale Retrieval Applications
Abstract
We explore a novel paradigm in learning binary codes for large-scale image retrieval applications. Instead of learning a single globally optimal quantization model as in previous approaches, we encode the database points in a data-specific manner using a bank of quantization models. Each individual database point selects the quantization model that minimizes its individual quantization error. We apply the idea of a bank of quantization models to data independent and data-driven hashing methods for learning binary codes, obtaining state-of-the-art performance on three benchmark datasets.
Cite
Text
Tung et al. "Bank of Quantization Models: A Data-Specific Approach to Learning Binary Codes for Large-Scale Retrieval Applications." IEEE/CVF Winter Conference on Applications of Computer Vision, 2015. doi:10.1109/WACV.2015.81Markdown
[Tung et al. "Bank of Quantization Models: A Data-Specific Approach to Learning Binary Codes for Large-Scale Retrieval Applications." IEEE/CVF Winter Conference on Applications of Computer Vision, 2015.](https://mlanthology.org/wacv/2015/tung2015wacv-bank/) doi:10.1109/WACV.2015.81BibTeX
@inproceedings{tung2015wacv-bank,
title = {{Bank of Quantization Models: A Data-Specific Approach to Learning Binary Codes for Large-Scale Retrieval Applications}},
author = {Tung, Frederick and Martinez, Julieta and Hoos, Holger H. and Little, James J.},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
year = {2015},
pages = {566-571},
doi = {10.1109/WACV.2015.81},
url = {https://mlanthology.org/wacv/2015/tung2015wacv-bank/}
}