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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 scalable re-ranking method for content-based image retrieval

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Author(s):
Guimaraes Pedronette, Daniel Carlos [1] ; Almeida, Jurandy [2, 3] ; Torres, Ricardo da S. [3]
Total Authors: 3
Affiliation:
[1] Univ Estadual Paulista UNESP, Dept Stat Appl Math & Comp, BR-13506900 Rio Claro, SP - Brazil
[2] Fed Univ Sao Paulo UNIFESP, Inst Sci & Technol, BR-12231280 Sao Jose Dos Campos, SP - Brazil
[3] Univ Campinas UNICAMP, RECOD Lab, IC, BR-13083852 Campinas, SP - Brazil
Total Affiliations: 3
Document type: Journal article
Source: INFORMATION SCIENCES; v. 265, p. 91-104, MAY 1 2014.
Web of Science Citations: 34
Abstract

Content-based Image Retrieval (CBIR) systems consider only a pairwise analysis, i.e., they measure the similarity between pairs of images, ignoring the rich information encoded in the relations among several images. However, the user perception usually considers the query specification and responses in a given context. In this scenario, re-ranking methods have been proposed to exploit the contextual information and, hence, improve the effectiveness of CBIR systems. Besides the effectiveness, the usefulness of those systems in real-world applications also depends on the efficiency and scalability of the retrieval process, imposing a great challenge to the re-ranking approaches, once they usually require the computation of distances among all the images of a given collection. In this paper, we present a novel approach for the re-ranking problem. It relies on the similarity of top-k lists produced by efficient indexing structures, instead of using distance information from the entire collection. Extensive experiments were conducted on a large image collection, using several indexing structures. Results from a rigorous experimental protocol show that the proposed method can obtain significant effectiveness gains (up to 12.19% better) and, at the same time, improve considerably the efficiency (up to 73.11% faster). In addition, our technique scales up very well, which makes it suitable for large collections. (C) 2014 Elsevier Inc. All rights reserved. (AU)

FAPESP's process: 11/11171-5 - Management Time Series of the e-Phenology
Grantee:Jurandy Gomes de Almeida Junior
Support type: Scholarships in Brazil - Post-Doctorate
FAPESP's process: 09/18438-7 - Large-scale classification and retrieval for complex data
Grantee:Ricardo da Silva Torres
Support type: Regular Research Grants
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: 09/05951-8 - High-dimensional multimedia indexing: application to image and video retrieval
Grantee:Eduardo Alves Do Valle Junior
Support type: Scholarships in Brazil - Post-Doctorate
FAPESP's process: 07/52015-0 - Approximation methods for visual computing
Grantee:Jorge Stolfi
Support type: Research Projects - Thematic Grants