Hybrid Hopfield Neural Network, Discrete Wavelet Transform and Huffman Coding for Image Recognition

This work presents a new solution to overcome the obstacle of using Hopfield Neural Network (HNN) with high level images than binary images. While HNN deals with bipolar system for direct input data, still it is not useful for graylevel or color images. Supposing for 8-bit gray-level image consists of 8-layers of bitplanes can be represented as bipolar data. Hence, it is possible to express each bitplane as single image. In this way HNN able to operate on gray level images with good results. However, storing huge data takes large space of storage. Therefore, Discrete Wavelet Transform (DWT) and Huffman Coding will be used in a hybridization system with HNN for reducing the large amount of data. This can be achieved by converting the eight states of bipolar weights for a minimum size of 3pixels vector into decimal representation to be ready for DWT and Huffman. In converging, the compressed weights will restore and reconverted into bipolar form. Experimental results showed the perfect performing of HNN for gray and color images recognition. This system tested on a large number of different samples of gray level images.