26 March 1993 Threshold competitive learning for vector quantization
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Image data compression is essential for a number of applications that involve transmission and storage. One technique that has been recently extensively investigated is vector quantization (VQ). One class of neural networks (NN) structures, namely competitive learning networks appears to be particularly suited for VQ. One main feature that characterizes NN training algorithms is that the VQ codewords are obtained in an adaptive manner. In this paper, a new competitive learning (CL) algorithm called the Threshold Competitive Learning (TCL) is introduced. The algorithm uses a threshold to determine the codewords to be updated after the presentation of each input vector. The threshold can be made variable as the training proceeds and more than one threshold can be used. The new algorithm can be easily combined with other NN training algorithms such as the Frequency-Sensitive competitive learning (FSCL) or the Kohonen Self-Organizing Feature Maps (KSFM). The new algorithm is shown to be efficient and yields results comparable to the famous traditional LBG algorithm.
© (1993) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ahmed S. EL-Behery, Ahmed S. EL-Behery, Samia A. Mashali, Samia A. Mashali, Ahmed M. Darwish, Ahmed M. Darwish, } "Threshold competitive learning for vector quantization", Proc. SPIE 1819, Digital Image Processing and Visual Communications Technologies in the Earth and Atmospheric Sciences II, (26 March 1993); doi: 10.1117/12.142200; https://doi.org/10.1117/12.142200


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