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Entree


A Multi-level Rank Correlation Measure for Image Retrieval

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Autor(es):
de Sa, Nikolas Gomes ; Valem, Lucas Pascotti ; Guimaraes Pedronette, Daniel Carlos ; Farinella, GM ; Radeva, P ; Braz, J ; Bouatouch, K
Número total de Autores: 7
Tipo de documento: Artigo Científico
Fonte: VISAPP: PROCEEDINGS OF THE 16TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS - VOL. 5: VISAPP; v. N/A, p. 9-pg., 2021-01-01.
Resumo

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. (AU)

Processo FAPESP: 17/25908-6 - Aprendizado fracamente supervisionado para análise de vídeos no domínio comprimido em tarefas de recuperação e classificação para alertas visuais
Beneficiário:João Paulo Papa
Modalidade de apoio: Auxílio à Pesquisa - Parceria para Inovação Tecnológica - PITE
Processo FAPESP: 18/15597-6 - Aplicação e investigação de métodos de aprendizado não-supervisionado em tarefas de recuperação e classificação
Beneficiário:Daniel Carlos Guimarães Pedronette
Modalidade de apoio: Auxílio à Pesquisa - Jovens Pesquisadores - Fase 2
Processo FAPESP: 19/11104-8 - Uma análise comparativa de métricas de correlação de ranqueamento para aprendizado fracamente supervisionado
Beneficiário:Nikolas Gomes de Sá
Modalidade de apoio: Bolsas no Brasil - Iniciação Científica