Solving the binding problem with feature integration theory

In this paper, we propose a neural network model of visual system based on the feature integration theory. The proposed model has a structure based on the hierarchical structure of visual system and selectiveness of information by visual attention. The proposed model consists of two stages: the feature recognition stage and the feature integration stage. In the feature recognition stage, there are two modules: the form recognition module and the color recognition module. In these modules, information of form and color is separately processed in parallel. The form recognition module is constructed of the neocognitron, and the color recognition module is constructed of the LVQ neural network. The feature integration stage is based on the feature integration theory, which is a representative theory for explaining all phenomena occurring in visual system as a consistent process. We carried out computer simulations and confirmed that the proposed model can recognize plural objects which have some features in vision and solve the binding problem.