Layered self-organizing map for image classification in unrestricted domains

Abstract

The inherent difficultly in unrestricted image domain classification is due to the many different features exhibited by images. Efforts made toward classification of abstract features tend to focus on a single attribute. Without a method of unifying descriptors, it becomes very difficult to perform multi-feature analysis. Extending the concept of the Self-Organizing Feature Map to include multiple competitive layers, it has been possible to create a new type of Artificial Neural Network capable of analyzing image and signal datasets with multiple feature descriptors concurrently in a powerful yet computationally light manner. Compared to standard CBIR retrieval approach, a marked increase in the precision of clustering of 13 points has been achieved, along with a reduction in computation time. © 2013 Springer-Verlag.