Authors:
Nikolas Gomes de Sá
;
Lucas Pascotti Valem
and
Daniel Carlos Guimarães Pedronette
Affiliation:
Department of Statistics, Applied Math. and Computing, São Paulo State University (UNESP), Rio Claro, Brazil
Keyword(s):
Content-based Image Retrieval, Rank Correlation, Unsupervised Learning, Information Retrieval.
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. T
he results are competitive or, in most of the cases, superior to the baselines, achieving significant effectiveness gains.
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