Online Hashing with Similarity Learning

TL;DR

This paper introduces a novel online hashing framework that maintains fixed hash functions and learns a parametric similarity function online, improving efficiency and accuracy in multi-label image retrieval.

Contribution

The proposed framework avoids updating binary codes by fixing hash functions and learning a bilinear similarity function online, which is a new approach in online hashing.

Findings

Outperforms state-of-the-art online hashing methods in accuracy.

Achieves higher efficiency in online multi-label image retrieval.

Demonstrates effectiveness on two multi-label image datasets.

Abstract

Online hashing methods usually learn the hash functions online, aiming to efficiently adapt to the data variations in the streaming environment. However, when the hash functions are updated, the binary codes for the whole database have to be updated to be consistent with the hash functions, resulting in the inefficiency in the online image retrieval process. In this paper, we propose a novel online hashing framework without updating binary codes. In the proposed framework, the hash functions are fixed and a parametric similarity function for the binary codes is learnt online to adapt to the streaming data. Specifically, a parametric similarity function that has a bilinear form is adopted and a metric learning algorithm is proposed to learn the similarity function online based on the characteristics of the hashing methods. The experiments on two multi-label image datasets show that our method is competitive or outperforms the state-of-the-art online hashing methods in terms of both accuracy and efficiency for multi-label image retrieval.