A New Localized Learning Scheme for Self-Organizing Feature Maps

R. Xiao, C.-H. Chang, and T. Srikanthan (Singapore)


Self-Organizing Feature Maps, ColorQuantizaiton, Artificial Neural Network.


Kohonen’s self organizing feature maps (SOFMs) have been used for color quantization in various image compression algorithms. Intuitively, the cause of slow convergence and poor generalization ability exhibited by some conventional SOFMs can be attributed to the ignorance of the localization of the adaptive processes. In this paper, a new learning algorithm is proposed where the rate of adaptation is based on the localized winning frequency of each individual neuron. Simulation results show that the proposed frequency adaptive learning algorithm can achieve a better reconstructed image quality and speed up the convergence by eight times for a 256-neuron network.

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