Constraint propagation neural networks for Huffman-Clowes scene labeling

The authors propose a three-layered neural network to perform Huffman-Clowes scene labeling. The proposed neural network uses the topology and the interconnections of neurons to achieve global consistency through propagating local constraints. The problem-solving knowledge is embedded in the topology as well as the connections between neurons in the network. A brief review of the Huffman-Clowes scene labeling scheme is presented. The proposed constraint propagation neural network is described. Several examples are given to illustrate the operation of the network. Time complexity analysis of the network is discussed. A comparison with conventional algorithms is given. The characteristics of the proposed neural networks are discussed. >