Neural Abstraction Pyramid: A hierarchical image understanding architecture

• Authors: Sven Behnke

• In Proceedings of International Joint Conference on Neural Networks (IJCNN'99) -- Washington, DC,  paper number #491, 1999.

 
• Abstract:

The recently introduced Neural Abstraction Pyramid is a hierarchical neural architecture for image interpretation that is inspired by the principles of information processing
found in the visual cortex. In this paper we present an unsupervised learning algorithm for it's connectivity based on Hebbian weight updates and competition. The algorithm
yields a sequence of feature detectors that produce increasingly abstract representations of the image content. These representations are distributed, sparse, and facilitate the
interpretation of the image.
We apply the algorithm to a dataset of handwritten digits, starting from local contrast detectors. The emerging feature detectors correspond to step edges, lines, strokes, curves,
and digit shapes. They can be used to reliably classify the digits.
 
• Full paper: ijcnn99.pdf