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Use and evaluation of methods for reclassifying and aggregating lists in different applications

Grant number: 16/10908-8
Support type:Scholarships in Brazil - Scientific Initiation
Effective date (Start): August 01, 2016
Effective date (End): July 31, 2017
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computer Systems
Principal Investigator:Daniel Carlos Guimarães Pedronette
Grantee:Matheus Gaseta
Home 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. Re-ranking algorithms have been used to exploit contextual information in image retrieval tasks. This work aims at investigating the use of re-ranking and rank aggregation methods in novel scenarios and applications. (AU)