A Selective Attention Based Method for Visual Pattern Recognition

Parallel pattern recognition requires great computational resources. It is desirable from an engineering point of view to achieve good performance with limited resources. For this purpose, we develop a serial model for visual pattern recognition based on the primate selective attention mechanism. The idea in selective attention is that not all parts of an image give us information. If we can attend to only the relevant parts, we can recognize the image more quickly and using less resources. We simulate the primitive, bottom-up attentive level of the human visual system with a saliency scheme, and the more complex, top-down, temporally sequential associative level with observable Markov models. In between, there is an artificial neural network that analyses image parts and generates posterior probabilities as observations to the Markov model. We test our model on a well-studied handwritten numeral recognition problem, and show how various performance related factors can be manipulated. Our results indicate the promise of this approach in complicated vision applications.