Segmentation using a competitive learning neural network for image coding

This paper describes a practical segmentation procedure using a simple competitive learning neural network to yield a complete segmentation suitable for segmentation-based image coding. Image segmentation is considered as a vector quantization problem. The procedure using the FSCL neural network for the vector quantization has the two main parts: primary and secondary segmentation. In the primary segmentation, an input image is finely segmented by the FSCL. In the secondary segmentation, a lot of small regions and similar regions with larger size generated in the preceding step are eliminated or merged together by the FSCL which performs partitioning and learning every input vector. Experimental results show that the procedure described here yields the reconstructed image of reasonably acceptable quality even at the low bit rate of 0.25 bit/pel.