A Novel Framework for Content-Based Image Retrieval Through Relevance Feedback Optimization

Content-based image retrieval remains an important research topic in many domains. It can be applied to assist specialists to improve the efficiency and accuracy of interpreting the images. However, it presents some intrinsic problems. This occurs due to the semantic interpretation of an image is still far to be reach, because it depends on the user’s perception about the image. Besides, each user presents different personal behaviors and experiences, which generates a high subjective analysis of a given image. To mitigate these problems the paper presents a novel framework for content-based image retrieval joining relevance feedback techniques with optimization methods. It is capable to not only capture the user intention, but also to tune the process through the optimization method according to each user. The experiments demonstrate the great applicability and efficacy of the proposed framework, which presented considerable gains of precision regarding similarity queries.