Abstract
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.
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© 1999 Springer-Verlag Berlin Heidelberg
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Kestler, H.A., Simon, S., Baune, A., Schwenker, F., Palm, G. (1999). Object Classification Using Simple, Colour Based Visual Attention and a Hierarchical Neural Network for Neuro-symbolic Integration. In: Burgard, W., Cremers, A.B., Cristaller, T. (eds) KI-99: Advances in Artificial Intelligence. KI 1999. Lecture Notes in Computer Science(), vol 1701. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48238-5_22
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DOI: https://doi.org/10.1007/3-540-48238-5_22
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