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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Combining re-ranking and rank aggregation methods for image retrieval

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Autor(es):
Guimaraes Pedronette, Daniel Carlos ; Torres, Ricardo da S.
Número total de Autores: 2
Tipo de documento: Artigo Científico
Fonte: MULTIMEDIA TOOLS AND APPLICATIONS; v. 75, n. 15, p. 9121-9144, AUG 2016.
Citações Web of Science: 2
Resumo

This paper presents novel approaches for combining re-ranking and rank aggregation methods aiming at improving the effectiveness of Content-Based Image Retrieval (CBIR) systems. Given a query image as input, CBIR systems retrieve the most similar images in a collection by taking into account image visual properties. In this scenario, accurately ranking collection images is of great relevance. Aiming at improving the effectiveness of CBIR systems, re-ranking and rank aggregation algorithms have been proposed. However, different re-ranking and rank aggregation approaches, applied to different image descriptors, may produce different and complementary image rankings. In this paper, we present four novel approaches for combining these rankings aiming at obtaining more effective results. Several experiments were conducted involving shape, color, and texture descriptors. The proposed approaches are also evaluated on multimodal retrieval tasks, considering visual and textual descriptors. Experimental results demonstrate that our approaches can improve significantly the effectiveness of image retrieval systems. (AU)

Processo FAPESP: 13/08645-0 - Reclassificação e agregação de listas para tarefas de recuperação de imagens
Beneficiário:Daniel Carlos Guimarães Pedronette
Linha de fomento: Auxílio à Pesquisa - Apoio a Jovens Pesquisadores