Exploiting Contextual Information for Image Re-Ranking and Rank Aggregation in Image Retrieval Tasks
Resumo
Em Sistemas de Recuperação de Images baseados no Conteúdo, a ordenação eficaz de imagens de uma coleção é de grande relevância, já que usuários estão interessados nas imagens retornadas nas primeiras posições. Entretanto, geralmente, estes sistemas analisam apenas pares de imagens para a geração das listas de resultados, ignorando importantes informações codificadas nos relacionamentos entre as imagens. Em contrapartida, a percepção dos usuários usualmente considera a especificação da consulta e os resultados em um dado contexto. Esse trabalho de doutorado propôs cinco novos algoritmos de re-ranking e rank aggregation com o objetivo de explorar informação contextual para melhorar a eficácia de sistemas de CBIR. Foram propostas também abordagens para combinar os métodos de re-ranking e rank aggregation e para executar eficientemente os métodos propostos em GPUs.
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