Hash Function Learning via Codewords

TL;DR

This paper introduces a hash learning framework using data-inferred codewords in Hamming space, capable of handling supervised, unsupervised, and semi-supervised tasks, with demonstrated advantages in image retrieval.

Contribution

It presents a novel hash learning approach leveraging codewords in Hamming space, unifying different supervision settings within a single framework.

Findings

Improved image retrieval performance over existing methods

Effective handling of various supervision scenarios

Demonstrated advantages in comparative experiments

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.