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An adaptive initialization technique for color quantization by self organizing feature map

An unsupervised learning network, such as the self organizing feature map (SOFM), has been applied successfully to color classification for image compression and pattern recognition. Like other vector quantization algorithms, the reconstruction quality and adaptation rate of the SOFM are sensitive to the neuron initialization. We propose an efficient new initialization method, whereby an excess number of neurons is defined and the neurons are adaptively pruned, merged and split within their lattice according to the spatial distribution of the input color pixels. Comparisons with conventional gray scale initialization using subsampling and butterfly jumping sequences show that the proposed method obtains good initial code vectors that can accelerate the convergence of the SOFM and improve the reconstructed image quality significantly.