Threshold competitive learning for vector quantization

Ahmed S. EL-Behery; Samia A. Mashali; Ahmed M. Darwish

doi:10.1117/12.142200

26 March 1993 Threshold competitive learning for vector quantization

Ahmed S. EL-Behery, Samia A. Mashali, Ahmed M. Darwish

Proceedings Volume 1819, Digital Image Processing and Visual Communications Technologies in the Earth and Atmospheric Sciences II; (1993) https://doi.org/10.1117/12.142200
Event: Applications in Optical Science and Engineering, 1992, Boston, MA, United States

Abstract

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.

Citation Download Citation

Ahmed S. EL-Behery, Samia A. Mashali, and 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); https://doi.org/10.1117/12.142200

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available