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Proceedings Paper

Neural network architectures for vector prediction
Author(s): Syed A. Rizvi; Lin-Cheng Wang; Qunfeng Liao; Nasser M. Nasrabadi
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Paper Abstract

A vector predictor is an integral part of the predictive vector quantization (PVQ) scheme. The performance of a predictor deteriorates as the vector dimension (block size) is increased. This makes it necessary to investigate new design techniques in order to design a vector predictor which gives better performance when compared to a conventional vector predictor. This paper investigates several neural network configurations which can be employed in order to design a vector predictor. The first neural network investigated in order to design the vector predictor is the multi-layer perceptron. The problem with multi-layer perceptron is the long convergence time which is undesirable when the on-line training of the neural network is required. Another neural network called functional link neural network has been shown to have fast convergence. The use of this network as a vector predictor is also investigated. The third neural network investigated is a recurrent type neural net. It is similar to the multi-layer perceptron except that a part of the predicted output is fed back to the hidden layer/layers in an attempt to further improve the current prediction. Finally, the use of a radial-basis function (RBF) network is also investigated for designing the vector predictor. The performances of the above mentioned neural network vector predictors are evaluated and compared with that of a conventional linear vector predictor.

Paper Details

Date Published: 28 March 1995
PDF: 12 pages
Proc. SPIE 2424, Nonlinear Image Processing VI, (28 March 1995); doi: 10.1117/12.205244
Show Author Affiliations
Syed A. Rizvi, SUNY/Buffalo (United States)
Lin-Cheng Wang, SUNY/Buffalo (United States)
Qunfeng Liao, SUNY/Buffalo (United States)
Nasser M. Nasrabadi, SUNY/Buffalo (United States)


Published in SPIE Proceedings Vol. 2424:
Nonlinear Image Processing VI
Edward R. Dougherty; Jaakko T. Astola; Harold G. Longbotham; Nasser M. Nasrabadi; Aggelos K. Katsaggelos, Editor(s)

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