Optimizing Top Precision Performance Measure of Content-Based Image Retrieval by Learning Similarity Function

TL;DR

This paper introduces a novel similarity learning method specifically designed to optimize the top precision measure in content-based image retrieval, addressing a gap in existing approaches.

Contribution

It proposes a new similarity learning approach that directly maximizes top precision by formulating it as a quadratic programming problem.

Findings

Outperforms existing similarity learning methods on benchmark datasets

Effectively optimizes top precision in image retrieval tasks

Demonstrates significant improvement in retrieval accuracy

Abstract

In this paper we study the problem of content-based image retrieval. In this problem, the most popular performance measure is the top precision measure, and the most important component of a retrieval system is the similarity function used to compare a query image against a database image. However, up to now, there is no existing similarity learning method proposed to optimize the top precision measure. To fill this gap, in this paper, we propose a novel similarity learning method to maximize the top precision measure. We model this problem as a minimization problem with an objective function as the combination of the losses of the relevant images ranked behind the top-ranked irrelevant image, and the squared Frobenius norm of the similarity function parameter. This minimization problem is solved as a quadratic programming problem. The experiments over two benchmark data sets show the advantages of the proposed method over other similarity learning methods when the top precision is used as the performance measure.