# Learning Hash Function through Codewords

## TL;DR

This paper introduces a versatile hash learning framework using codewords in Hamming space, capable of supervised, unsupervised, and semi-supervised scenarios, with efficient optimization and demonstrated improvements in image retrieval.

## Contribution

It presents a novel hash learning method leveraging data-inferred codewords and a regularization technique, adaptable to various supervision settings, with an efficient optimization algorithm.

## Key findings

- Outperforms existing methods in content-based image retrieval
- Supports supervised, unsupervised, and semi-supervised learning
- Uses efficient SVM-based optimization with closed-form solutions

## Abstract

In this paper, we propose a novel hash learning approach that has the following main distinguishing features, when compared to past frameworks. First, the codewords are utilized in the Hamming space as ancillary techniques to accomplish its hash learning task. These codewords, which are inferred from the data, attempt to capture grouping aspects of the data's hash codes. Furthermore, the proposed framework is capable of addressing supervised, unsupervised and, even, semi-supervised hash learning scenarios. Additionally, the framework adopts a regularization term over the codewords, which automatically chooses the codewords for the problem. To efficiently solve the problem, one Block Coordinate Descent algorithm is showcased in the paper. We also show that one step of the algorithms can be casted into several Support Vector Machine problems which enables our algorithms to utilize efficient software package. For the regularization term, a closed form solution of the proximal operator is provided in the paper. A series of comparative experiments focused on content-based image retrieval highlights its performance advantages.