A Percepção Humana na Recuperação de Imagens por Conteúdo

Joselene MarquesAgma Juci Machado Traina

The Relevance Feedback (RR) approach is a powerful mechanism to refine and improve the techniques for content-based image retrieval (CBIR) considering the subjectivity introduced by the human analysis. Traditionally, in this process the human analyst weighs the images retrieved, considering their degree of relevance to the query posed. By doing so, the subjectivity of human perception is introduced in the CBIR, and the semantic gap inherent to this process is diminished. This work discusses the use of Relevance Feedback in a real CBIR system under development at the ICMC-USP, which aims at minimizing the negative effect on the user acceptance of a CBIR system when false negative images are retrieved. A new approach on RR is proposed, which aims at taking advantage of the computation of the similarity function between the images to improve the set of relevant images retrieved.

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