A Multi-level Rank Correlation Measure for Image Retrieval

• Autor(a) principal: Sa, Nikolas Gomes de [UNESP]
• Data de Publicação: 2021
• Outros Autores: Valem, Lucas Pascotti [UNESP], Guimaraes Pedronette, Daniel Carlos [UNESP], Farinella, G. M., Radeva, P., Braz, J., Bouatouch, K.
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.5220/0010220903700378; http://hdl.handle.net/11449/218606
• topic: Content-based Image Retrieval; Rank Correlation; Unsupervised Learning; Information Retrieval

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.