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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 unified model for accelerating unsupervised iterative re-ranking algorithms

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Pisani, Flavia [1, 2] ; Pascotti Valem, Lucas [3] ; Guimaraes Pedronette, Daniel Carlos [3] ; da S. Torres, Ricardo [4] ; Borin, Edson [2] ; Breternitz, Mauricio [5]
Total Authors: 6
[1] Pontifical Catholic Univ Rio de Janeiro, Dept Informat, Rio De Janeiro, RJ - Brazil
[2] Univ Estadual Campinas, Inst Comp, Av Albert Einstein 1251, Cidade Univ, Campinas, SP - Brazil
[3] Sao Paulo State Univ, Dept Stat Appl Math & Comp, Rio Claro, SP - Brazil
[4] NTNU Norwegian Univ Sci & Technol, Dept ICT & Nat Sci, Alesund - Norway
[5] ISCTE IUL Lisbon Univ Inst, ISTAR IUL, Lisbon - Portugal
Total Affiliations: 5
Document type: Journal article
Web of Science Citations: 0

Despite the continuous advances in image retrieval technologies, performing effective and efficient content-based searches remains a challenging task. Unsupervised iterative re-ranking algorithms have emerged as a promising solution and have been widely used to improve the effectiveness of multimedia retrieval systems. Although substantially more efficient than related approaches based on diffusion processes, these re-ranking algorithms can still be computationally costly, demanding the specification and implementation of efficient big multimedia analysis approaches. Such demand associated with the significant potential for parallelization and highly effective results achieved by recently proposed re-ranking algorithms creates the need for exploiting efficiency vs effectiveness trade-offs. In this article, we introduce a class of unsupervised iterative re-ranking algorithms and present a model that can be used to guide their implementation and optimization for parallel architectures. We also analyze the impact of the parallelization on the performance of four algorithms that belong to the proposed class: Contextual Spaces, RL-Sim, Contextual Re-ranking, and Cartesian Product of Ranking References. The experiments show speedups that reach up to 6.0x, 16.1x, 3.3x, and 7.1x for each algorithm, respectively. These results demonstrate that the proposed parallel programming model can be successfully applied to various algorithms and used to improve the performance of multimedia retrieval systems. (AU)

FAPESP's process: 14/50715-9 - Characterizing and predicting biomass production in sugarcane and eucalyptus plantations in Brazil
Grantee:Rubens Augusto Camargo Lamparelli
Support type: Research Grants - Research Partnership for Technological Innovation - PITE
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
FAPESP's process: 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?
Grantee:Alexandre Xavier Falcão
Support type: Research Projects - Thematic 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: 13/50155-0 - Combining new technologies to monitor phenology from leaves to ecosystems
Grantee:Leonor Patricia Cerdeira Morellato
Support type: Research Program on Global Climate Change - University-Industry Cooperative Research (PITE)
FAPESP's process: 15/24494-8 - Communications and processing of big data in cloud and fog computing
Grantee:Nelson Luis Saldanha da Fonseca
Support type: Research Projects - Thematic Grants