Explorando abordagens evolutivas para recuperação de imagens baseada em conteúdo

Abstract

The process of organization and retrieval of images presents numerous problems to be addressed, to understand the subjective meaning of a visual query through numerical parameters that can be extracted and compared by a computer is a challenge. The disparity between the comparison performed by the machine and that in fact the human being interprets a visual query is known as semantic gap. Several studies in the literature techniques to address this gap. In this context, relevance feedback (RF) is an effective approach to capture user intent and reduce the difference between semantic concepts and the visual characteristics of an image. However, the semantic gap is still a challenge to be overcome, therefore, the present work aims to study, analysis and proposal for a new framework for image retrieval through relevance feedback combined with evolutionary algorithms aiming to bring the user’s expectation througth the results returned by the retrieval of images through the his intention and definition of the most appropriate parameters. To this end, the main idea of the work is to insert learning memory in a content-based image retrieval system for this by storing the user interaction data with the system profiles so that later these data are able to provide parameters for the system “learn” and respond to personalized user and need. Thus, contributing to reduction of existing semantic gap between the results of a retrieval operation and indeed expected images for a specific context, improving the efficiency and effectiveness of the retrieval process.