Feature selection for neural network recognition
The authors present a system designed to help in the development of image recognition applications, using a general neural-network classifier and an algorithm for selecting effective image features given a small number of samples. Input to the system consists of a number of primitive image features...
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| Published in: | IEEE-INNS International Joint Conference on Neural Networks - Singapore, 1991 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
01-01-1992
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| Online Access: | Get full text |
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| Summary: | The authors present a system designed to help in the development of image recognition applications, using a general neural-network classifier and an algorithm for selecting effective image features given a small number of samples. Input to the system consists of a number of primitive image features computed directly from pixel values. The feature selection subsystem generates an image recognition feature vector by operations on the primitive features. It uses a combination of rule-based techniques and statistical heuristics to select the best features. The authors propose a quality statistic function which is based on sample values for each primitive feature. The parameters of this function were decided, and the authors experimented on several different target image groups using this function. Recognition rates were perfect in each case. |
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| Bibliography: | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 |
| ISBN: | 0780302273 9780780302273 |