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Image indexing and retrieval using an ART‐2A neural network architecture
Author(s) -
Fernandes de Mello Rodrigo,
Bueno Josiane Maria,
Senger Luciano José,
Yang Laurence T.
Publication year - 2008
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.20149
Subject(s) - computer science , search engine indexing , image retrieval , semantics (computer science) , information retrieval , categorization , relation (database) , artificial intelligence , content based image retrieval , visual word , artificial neural network , image (mathematics) , architecture , pattern recognition (psychology) , data mining , art , visual arts , programming language
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. © 2008 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 18, 202–208, 2008