Probabilistic spectral feature extraction technique for neural networks

Young Ro Yoon; Okan K. Ersoy

doi:10.1117/12.162026

29 October 1993 Probabilistic spectral feature extraction technique for neural networks

Young Ro Yoon, Okan K. Ersoy

Proceedings Volume 2032, Neural and Stochastic Methods in Image and Signal Processing II; (1993) https://doi.org/10.1117/12.162026
Event: SPIE's 1993 International Symposium on Optics, Imaging, and Instrumentation, 1993, San Diego, CA, United States

Abstract

Artificial neural net models have been studied for many years in the hope of achieving human- like performance in the fields of speech, image recognition and pattern recognition. For high performance and for controlling the size of the network, the input information must be preprocessed before being fed into the neural network. In this paper, a probabilistic spectral feature extraction technique (PSFET) with multiview spectral representations and its applications are described. During training and testing, the PSFET allows efficient extraction of useful information in addition to generating an input vector size for best classification performance by the following neural network. Experimental results indicate that the performance of the neural network increases in classification accuracy when PSFET is used at the input. The network also generalizes better.

Citation Download Citation

Young Ro Yoon and Okan K. Ersoy "Probabilistic spectral feature extraction technique for neural networks", Proc. SPIE 2032, Neural and Stochastic Methods in Image and Signal Processing II, (29 October 1993); https://doi.org/10.1117/12.162026

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KEYWORDS

Neural networks

Quantization

Binary data

Feature extraction

Distance measurement

Data storage

Pattern recognition

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