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Feature selection and aggregation based on rank measures

Grant number: 22/07349-8
Support Opportunities:Scholarships in Brazil - Master
Effective date (Start): August 01, 2022
Effective date (End): April 30, 2024
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal Investigator:Daniel Carlos Guimarães Pedronette
Grantee:Ademir Moreno Junior
Host Institution: Instituto de Geociências e Ciências Exatas (IGCE). Universidade Estadual Paulista (UNESP). Campus de Rio Claro. Rio Claro , SP, Brazil
Associated research grant:18/15597-6 - Aplication and investigation of unsupervised learning methods in retrieval and classification tasks, AP.JP2


Despite the significant recent advances in feature extraction methods based on deep learning approaches, effectively retrieving images still remains a challenge in various scenarios. Such complexity is mainly associated with the diverse aspects involved in visual perception, which usually can not be encoded by a single feature. Given the myriad of available features, several fusion approaches have been investigated aiming at producing more effective retrieval results. Despite the success of fusion approaches, another crucial task consists in defining what features to combine. Selecting features (or rankers) capable of producing an effective retrieval result consists of a difficult task, especially in unsupervised scenarios, where no information about the effectiveness of the feature is available. This project aims to address this problem by investigating an unsupervised selection framework based on ranking information. The proposed methodology aims to investigate the use of recently proposed rank correlation measures for analyzing complementary information and recent effectiveness estimation measures to identify the most effective combinations. (AU)

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