Model-based 2-D object recognition is investigated by using neural network. Object recognition is treated as a subgraph matching. A neural network system is proposed to complete subgraph matching. The system consists of a large Hopfield network, called global network, and several small Hopfield networks, called subnetworks. The system starts with a randomly set initial state of the global network. The subnetworks are dynamically created based on the stable output state of the global network and then the outputs of the subnetworks are feedbacked to the global network to reset its initial state. This process continues until the whole system is stabilized, where the optimal subgraph matching is obtained. This method avoids the local minimum problem from using a single Hopfield network and also uses much less calculating time than simulated annealing algorithm. Computer simulation is done to verify it.
Mengkang Peng, Mengkang Peng,
"Neural network architecture for object recognition", Proc. SPIE 2304, Neural and Stochastic Methods in Image and Signal Processing III, (30 June 1994); doi: 10.1117/12.179232; https://doi.org/10.1117/12.179232