Object Classification Using Simple, Colour Based Visual Attention and a Hierarchical Neural Network for Neuro-symbolic Integration

An object classification system built of a simple colour based visual attention method, and a prototype based hierarchical classifier is established as a link between subsymbolic 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 subimages were utilized as features.With the currently very simple feature extraction method, classification accuracies in the range of 69% to 90% were attained.