Utilização de condições de contorno para combinação de múltiplos descritores em consultas por similaridade

Abstract

Complex data, like images, face semantic problems in your queries that might compromise results quality. Such problems have their source on the differences found between the semantic interpretation of the data and its low level machine language. In this representation are utilized feature vectors that describe intrinsic characteristics (like color, shape and texture) into qualifying attributes. Analyzing the similarity in complex data, perceives that these intrinsic characteristics complemented the representation of data, as well as is carried out by human perception and for this reason the use of multiple descriptors tend to improve the ability of discrimination data. In this context, another relevant fact is that in a data set, some subsets may present essential specific intrinsic characteristics to better show their rest of the data elements. Based in such premises, this work proposes the use of boundary conditions to identify these subsets and then use the best descriptor combination balancing for each of these, aiming to decrease the existing semantic gap in similarity queries. Throughout the conducted experiments the use of the proposed technique had better results when compared to use individual descriptor using the same boundary conditions and also using descriptors combination for the whole set without the use of boundary conditions.