A Multi-level Rank Correlation Measure for Image Retrieval

Nikolas Gomes de Sá, Lucas Pascotti Valem, Daniel Carlos Guimarães Pedronette

2021

Abstract

Accurately ranking the most relevant elements in a given scenario often represents a central challenge in many applications, composing the core of retrieval systems. Once ranking structures encode relevant similarity information, measuring how correlated are two rank results represents a fundamental task, with diversified applications. In this work, we propose a new rank correlation measure called Multi-Level Rank Correlation Measure (MLCM), which employs a novel approach based on a multi-level analysis for estimating the correlation between ranked lists. While traditional weighted measures assign more relevance to top positions, our proposed approach goes beyond by considering the position at different levels in the ranked lists. The effectiveness of the proposed measure was assessed in unsupervised and weakly supervised learning tasks for image retrieval. The experimental evaluation considered 6 correlation measures as baselines, 3 different image datasets, and multiple features. The results are competitive or, in most of the cases, superior to the baselines, achieving significant effectiveness gains.

Paper Citation

in Harvard Style

Gomes de Sá N., Valem L. and Pedronette D. (2021). A Multi-level Rank Correlation Measure for Image Retrieval. In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP; ISBN 978-989-758-488-6, SciTePress, pages 370-378. DOI: 10.5220/0010220903700378

in Bibtex Style

@conference{visapp21,
author={Nikolas Gomes de Sá and Lucas Pascotti Valem and Daniel Carlos Guimarães Pedronette},
title={A Multi-level Rank Correlation Measure for Image Retrieval},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP},
year={2021},
pages={370-378},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010220903700378},
isbn={978-989-758-488-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP
TI - A Multi-level Rank Correlation Measure for Image Retrieval
SN - 978-989-758-488-6
AU - Gomes de Sá N.
AU - Valem L.
AU - Pedronette D.
PY - 2021
SP - 370
EP - 378
DO - 10.5220/0010220903700378
PB - SciTePress