A neural network architecture for trademark image retrieval

This paper describes a novel massively parallel connectionist architecture for image retrieval. The proposed search engine of the system consists of associative memory nodes connected by information channels which convey symbolic messages. Symbolic information stored inside the system is obtained using gestalt feature extraction methods which capture multiple representations of images. In this paper, we summarise our feature extraction method and then we describe the connection schemata of the system, training process as well as how such a system can be utilised to capture perceptual similarity of trademark images. Finally, we present results obtained during evaluation of the system.