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The retrieval of images from databases using features automatically extracted from the stored material takes advantage of ali information contained in the images, and it precludes human analysis. The features extracted are typically arrays of numbers that are used by index structures as points in an n-dimensional space, where n is the size of the feature array. Recently, many indexing methods for the image data type have been studied and developed on the context of Data Base Management Systems. Such methods are known as Spatial Data Structures or Multidimensional Access Methods. This work briefly introduces how "Images" are defined as an abstract data type in the SIRIUS Data Model, and how feature extraction algorithms can be associated to this data type. The approach is aimed toward a medical domain application context Therefore, the most important spatial data types are studied, describing their properties, common applications and indexing methods. The methods R-Tree and TV-Tree are described in detail. Finally, the results of the tests performed using these methods for point data sets within the dimensions 2 and 255 are presented. It was seen that the TV-Tree method is better suited for applications that need to work with point data in distinct and high dimensions. Thus, the TV-tree method can be used in the SIRIUS/GO Object Manager.

Algoritmos para Indexação de Dados Espaciais Pontuais em Gerenciadores de Objetos