Publicação:
Unsupervised manifold learning using Reciprocal kNN Graphs in image re-ranking and rank aggregation tasks

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2014-02-01

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Elsevier B.V.

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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.

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Inglês

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Image And Vision Computing. Amsterdam: Elsevier Science Bv, v. 32, n. 2, p. 120-130, 2014.

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