| Full text | |
| Author(s): |
Total Authors: 3
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| Affiliation: | [1] Univ Estadual Paulista UNESP, Dept Stat Appl Math & Comp, BR-13506900 Rio Claro, SP - Brazil
[2] Univ Campinas UNICAMP, RECOD Lab, IC, BR-13083852 Campinas, SP - Brazil
[3] Fed Univ Sao Paulo UNIFESP, Inst Sci & Technol, BR-12231280 Sao Jose Dos Campos, 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 Opportunities: | Scholarships in Brazil - Post-Doctoral |
| FAPESP's process: | 13/08645-0 - Re-Ranking and rank aggregation approaches for image retrieval tasks |
| Grantee: | Daniel Carlos Guimarães Pedronette |
| Support Opportunities: | 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 Opportunities: | Scholarships in Brazil - Post-Doctoral |
| FAPESP's process: | 07/52015-0 - Approximation methods for visual computing |
| Grantee: | Jorge Stolfi |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 09/18438-7 - Large-scale classification and retrieval for complex data |
| Grantee: | Ricardo da Silva Torres |
| Support Opportunities: | Regular Research Grants |