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|Title:||Unsupervised manifold learning using reciprocal kNN graphs in image re-ranking and rank aggregation tasks|
|Author:||Pedronette, Daniel Carlos Guimarães|
Penatti, Otávio A. B.
Torres, Ricardo da S.
|Abstract:||In this paper, we present an unsupervised distance learning approach for improving the effectiveness of image retrieval tasks. We propose a Reciprocal kNN Graph algorithm that considers the relationships among ranked lists in the context of a k-reciprocal neighborhood. The similarity is propagated among neighbors considering the geometry of the dataset manifold. The proposed method can be used both for re-ranking and rank aggregation tasks. Unlike traditional diffusion process methods, which require matrix multiplication operations, our algorithm takes only a subset of ranked lists as input, presenting linear complexity in terms of computational and storage requirements. We conducted a large evaluation protocol involving shape, color, and texture descriptors, various datasets, and comparisons with other post-processing approaches. The re-ranking and rank aggregation algorithms yield better results in terms of effectiveness performance than various state-of-the-art algorithms recently proposed in the literature, achieving bull's eye and MAP scores of 100% on the well-known MPEG-7 shape dataset (C) 2013 Elsevier B.V. All rights reserved.|
In this paper, we present an unsupervised distance learning approach for improving the effectiveness of image retrieval tasks. We propose a Reciprocal kNN Graph algorithm that considers the relationships among ranked lists in the context of a k-reciprocal
Ensino à distância
Recuperação de imagens baseada em conteúdo
|Citation:||Image And Vision Computing. Elsevier Science Bv, v. 32, n. 2, n. 120, n. 130, 2014.|
|Appears in Collections:||IC - Artigos e Outros Documentos|
FT - Artigos e Outros Documentos
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