Execução Eficiente de Métodos de Reclassificação e Agregação de Listas

• Grant number: 14/04220-8
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: May 01, 2014
• End date: 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.

Scientific publications

(4)

(The scientific publications listed on this page originate from the Web of Science or SciELO databases. Their authors have cited FAPESP grant or fellowship project numbers awarded to Principal Investigators or Fellowship Recipients, whether or not they are among the authors. This information is collected automatically and retrieved directly from those bibliometric databases.)

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, OCT 15 2018. (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., 2017-01-01. (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., 2018-10-15. (16/06441-7, 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., 2016-01-01. (13/08645-0)