Image indexing and retrieval using an ART-2A neural network architecture

✍ Scribed by Rodrigo Fernandes de Mello; Josiane Maria Bueno; Luciano José Senger; Laurence T. Yang

• Publisher: John Wiley and Sons
• Year: 2008
• Tongue: English
• Weight: 292 KB
• Volume: 18
• Category: Article
• ISSN: 0899-9457
• DOI: 10.1002/ima.20149

✦ Synopsis

Abstract

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