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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

A correlation graph approach for unsupervised manifold learning in image retrieval tasks

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Author(s):
Guimaraes Pedronette, Daniel Carlos ; Torres, Ricardo da S.
Total Authors: 2
Document type: Journal article
Source: Neurocomputing; v. 208, n. SI, p. 66-79, OCT 5 2016.
Web of Science Citations: 9
Abstract

Effectively measuring the similarity among images is a challenging problem in image retrieval tasks due to the difficulty of considering the dataset manifold. This paper presents an unsupervised manifold learning algorithm that takes into account the intrinsic dataset geometry for defining a more effective distance among images. The dataset structure is modeled in terms of a Correlation Graph (CG) and analyzed using Strongly Connected Components (SCCs). While the Correlation Graph adjacency provides a precise but strict similarity relationship, the Strongly Connected Components analysis expands these relationships considering the dataset geometry. A large and rigorous experimental evaluation protocol was conducted for different image retrieval tasks. The experiments were conducted in different datasets involving various image descriptors. Results demonstrate that the manifold learning algorithm can significantly improve the effectiveness of image retrieval systems. The presented approach yields better results in terms of effectiveness than various methods recently proposed in the literature. (C) 2016 Elsevier B.V. All rights reserved. (AU)

FAPESP's process: 13/08645-0 - Re-Ranking and rank aggregation approaches for image retrieval tasks
Grantee:Daniel Carlos Guimarães Pedronette
Support type: Research Grants - Young Investigators Grants
FAPESP's process: 13/50169-1 - Towards an understanding of tipping points within tropical South American biomes
Grantee:Ricardo da Silva Torres
Support type: Research Grants - Research Partnership for Technological Innovation - PITE