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Queries dealing with complex data, such as images, face semantic problems that might compromise results quality. Such problems have their source on the differences found between the semantic interpretation of the data and their low level machine code representation. The descriptors utilized in such representation translate intrinsic characteristics of the data usually color, shape and texture into qualifying attributes. Different descriptors represent different intrinsic characteristics that can get different aspects of the data while processing a similarity comparison among them. Therefore, the use of multiple descriptors tends to improve data separation and categorization, if compared to the use of a single descriptor. Another relevant fact is that some specific intrinsic characteristics are essential for identifying a subset of the data. Based on such premises, this work proposes the use of boundary conditions to identify image subsets and then use the best descriptor combination for each of these subsets aimed at decreasing the existing "semantic gap". Throughout the conducted experiments, the use of the proposed technique had better results when compared to individual descriptor use employing the same boundary conditions and to various descriptors combination without the use of boundary conditions.

Using Boundary Conditions for Combining Multiple Descriptors in Similarity Based Queries