Object classification with simple visual attention and a hierarchical neural network for subsymbolic-symbolic coupling

An object classification system using a simple color based visual attention method, and a prototype based hierarchical classifier is established as a link between sub-symbolic and symbolic data processing. During learning the classifier generates a hierarchy of prototypes. These prototypes constitute a taxonomy of objects. By assigning confidence values to the prototypes a classification request may also return symbols with confidence values. For performance evaluation the classifier was applied to the task of visual object categorization of three data sets, two real-world and one artificial. Orientation histograms on sub-images were utilized as features. With the currently very simple feature extraction method, classification accuracies of about 75% to 90% were attained