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Lists efficient re-ranking and rank aggregation methods

Grant number: 14/04220-8
Support Opportunities:Scholarships in Brazil - Scientific Initiation
Effective date (Start): May 01, 2014
Effective date (End): December 31, 2016
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computer Systems
Principal Investigator:Daniel Carlos Guimarães Pedronette
Grantee:Lucas Pascotti Valem
Host Institution: Instituto de Geociências e Ciências Exatas (IGCE). Universidade Estadual Paulista (UNESP). Campus de Rio Claro. Rio Claro , SP, Brazil
Associated research grant:13/08645-0 - Re-Ranking and rank aggregation approaches for image retrieval tasks, AP.JP

Abstract

Content-Based Image Retrieval (CBIR) systems aims at retrieving the most similar images in a collection by taking into account image visual properties. Users are interested in the images placed at the first positions of the returned ranked lists, which usually are the most relevant ones. Therefore, accurately ranking collection images is of great relevance. However, in general, CBIR approaches perform only pairwise image analysis, that is, they compute similarity (or distance) measures considering only pairs of images, ignoring the rich information encoded in the relationships among images. Aiming at improving the effectiveness of CBIR systems, re-ranking and rank aggregation algorithms have been proposed. The main goal of this project consists in the parallel implementation of a re-ranking method and profiling actions on heterogeneous computing environments, involving CPUs and GPUs.

News published in Agência FAPESP Newsletter about the scholarship:
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Scientific publications (4)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
VALEM, LUCAS PASCOTTI; GUIMARAES PEDRONETTE, DANIEL CARLOS; ALMEIDA, JURANDY. Unsupervised similarity learning through Cartesian product of ranking references. PATTERN RECOGNITION LETTERS, v. 114, n. SI, p. 41-52, . (14/04220-8, 17/02091-4, 16/06441-7, 13/08645-0)
VALEM, LUCAS PASCOTTI; GUIMARAES PEDRONETTE, DANIEL CARLOS; ACM. An Unsupervised Distance Learning Framework for Multimedia Retrieval. PROCEEDINGS OF THE 2017 ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA RETRIEVAL (ICMR'17), v. N/A, p. 5-pg., . (14/04220-8, 13/08645-0)
VALEM, LUCAS PASCOTTI; GUIMARAES PEDRONETTE, DANIEL CARLOS; ALMEIDA, JURANDY. Unsupervised similarity learning through Cartesian product of ranking references. PATTERN RECOGNITION LETTERS, v. 114, p. 12-pg., . (16/06441-7, 14/04220-8, 13/08645-0, 17/02091-4)
VALEM, LUCAS PASCOTTI; GUIMARAES PEDRONETTE, DANIEL CARLOS; IEEE. Unsupervised Similarity Learning through Cartesian Product of Ranking References for Image Retrieval Tasks. 2016 29TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), v. N/A, p. 8-pg., . (14/04220-8, 13/08645-0)

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