An interactive model for structural pattern recognition based on the Bayes classifier.

This paper presents an interactive model for structural pattern recognition based on a naïve Bayes classifier. In some applications, the automatically computed correlation between local parts of two images is not good enough. Moreover, humans are very good at locating and mapping local parts of images although any kind of global transformations had been applied to these images. In our model, the user interacts on the automatically obtained correlation (or correspondences between local parts) and helps the system to find the best correspondence while the global transformation parameters are automatically recomputed. The model is based on a Bayes classifier in which the human interaction is properly modelled and embedded in the model. We show that with little human interaction, the quality of the returned correspondences and global transformation parameters drastically increases