Mineração visual de imagens aliada a consultas pelos k-vizinhos diversos mais próximos: flexibilizando e maximizando o entendimento de consultas por conteúdo de imagens

Abstract

Content-Based Image Retrieval systems use visual information like color, shape and texture to represent images in feature vectors. The numerical representation found for the images is used in query execution through a metric to evaluate the distance between vectors. In general, there is an inconsistency in the evaluation of similarity between images according to human perception and the results computed by CBIR systems, which is called Semantic Gap. One way to overcome this problem is by the addition of a diversity factor in query execution, allowing the user to specify a degree of dissimilarity between the resulting images and changing the query result. Adding diversity in consultation, however, requires high computational cost and the reduction of possible subsets to be analyzed is a difficult task to be understood by the user. This masters degree thesis aims to make use of Visual Data Mining techniques applied to queries in CBIR systems, improving the interpretability of the measure of similarity and diversity, as well as the relevance of the result according to the judgment and prior knowledge of the user. The user takes an active role in the retrieval of images by their content, guiding its result and, consequently, reducing the Semantic Gap. Additionally, a better understanding of the diversity and similarity factors involved in the query is supported by visualization and interaction techniques.