Abstract: We propose a new computational model for object recognition based on the vision cognitive findings. Feature integration theory offers the roadmap for our computing model. We construct the learning procedure to acquire necessary pre-knowledge for the recognition network on the basis of the hypothesis-maximum entropy principle. With the recognition network, we can bind the low-level image features and the high-level knowledge. Fundamental concepts and principles of conditional random fields are employed to model the binding process. We apply our model to real object recognition problem and evaluate it on the benchmark image databases to show its satisfactory performance.

Key words: conditional random fields, feature binding, feature integration, object recognition