1000 resultados para ART-2A


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Traditional content-based image retrieval (CBIR) systems use low-level features such as colors, shapes, and textures of images. Although, users make queries based on semantics, which are not easily related to such low-level characteristics. Recent works on CBIR confirm that researchers have been trying to map visual low-level characteristics and high-level semantics. The relation between low-level characteristics and image textual information has motivated this article which proposes a model for automatic classification and categorization of words associated to images. This proposal considers a self-organizing neural network architecture, which classifies textual information without previous learning. Experimental results compare the performance results of the text-based approach to an image retrieval system based on low-level features. (c) 2008 Wiley Periodicals, Inc.

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En la anteport.: "Encyclopédie méthodique ou par ordre de matières, par une Société de gens de lettres, de savants et d'artistes, précédé d'un vocabulaire universel, servant de table pour tout l'ouvrage, ornée des portraits de MM. Diderot & d'Alembert, premieurs editeurs de l'Encyclopédie"

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