Artículos de revistas
Unsupervised Manifold Learning Using Reciprocal Knn Graphs In Image Re-ranking And Rank Aggregation Tasks
Registro en:
Image And Vision Computing. , v. 32, n. 2, p. 120 - 130, 2014.
2628856
10.1016/j.imavis.2013.12.009
2-s2.0-84893158493
Autor
Guimaraes Pedronette D.C.
Penatti O.A.B.
Da S. Torres R.
Institución
Resumen
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. 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