# Global Hashing System for Fast Image Search

## TL;DR

This paper introduces a novel global hashing system that enhances fast image search by combining low-dimensional embedding with a modified global positioning approach, outperforming existing methods especially with longer bit codes.

## Contribution

It proposes a new two-step hashing process with data-independent and data-dependent methods, improving image retrieval accuracy and efficiency in large datasets.

## Key findings

- Data-dependent method outperforms others across datasets from 100k to 10M.
- Incorporating orthogonality improves performance with longer bit codes.
- The approach effectively balances information loss in embedding and positioning steps.

## Abstract

Hashing methods have been widely investigated for fast approximate nearest neighbor searching in large data sets. Most existing methods use binary vectors in lower dimensional spaces to represent data points that are usually real vectors of higher dimensionality. We divide the hashing process into two steps. Data points are first embedded in a low-dimensional space, and the global positioning system method is subsequently introduced but modified for binary embedding. We devise dataindependent and data-dependent methods to distribute the satellites at appropriate locations. Our methods are based on finding the tradeoff between the information losses in these two steps. Experiments show that our data-dependent method outperforms other methods in different-sized data sets from 100k to 10M. By incorporating the orthogonality of the code matrix, both our data-independent and data-dependent methods are particularly impressive in experiments on longer bits.