Eigenspace Method by Autoassociative Networks for Object Recognition
This paper studies on a new eignespace method which employs autoassociative networks for object recognition. Five layered autoassociative network is available to obtain a manifold on the minimum square error hypersurface which approximates a distribution of learning sample. Recognition experiments were performed to show that the manifold of rotating object is obtained by learning and the objects, such as a mouse and a stapler, are correctly recognized by the autoassociative networks. It is also shown that the accuracy of approximating closed manifold and the accuracy of recognition are improved by emploing multiple autoassociative networks each of which is trained by a partition of the learning sample.The property and the advantage of the five layered autoassociative network are demonstrated by a comparative study with the nearest neighbor method and the eigenspace method.
KeywordsObject Recognition Input Pattern Closed Manifold Neighbor Method Learning Sample
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