Fingerprint image compression by a natural clustering neural network
W. Chang, Hamdy Soliman, Andrew H. Sung · 2002
A self-organizing neural network performing learning vector quantization (LVQ) is proposed in this paper to compress image data in still pictures. The advantages of our model are its low training time complexity, high utilization of neurons, robust clustering capability, and simple computation; further, a VLSI implementation is highly feasible. By unsupervised learning, our LVQ neural model finds near-optimal clustering from image data and builds a compression codebook in the synaptic weights. The compression results are competitive comparing with the currently popular transform codings such as JPEG and wavelet methods. The neural codebook trained by a few pictures can be used to compression other pictures efficiently. Special image types such as the fingerprints exhibit this property in our experiments. Other experiments involve some filtering effects and techniques to enhance the neural codebook learning to yield higher picture quality.>