Explicit Modelling of Invariances in Bernoulli Mixtures for Binary Images

Abstract

Bernoulli mixture models have been recently proposed as simple yet powerful probabilistic models for binary images in which each image pattern is modelled by a different Bernoulli prototype (component). A possible limitation of these models, however, is that usual geometric transformations of image patterns are not explicitly modelled and, therefore, each natural transformation of an image pattern has to be independently modelled using a different, rigid prototype. In this work, we propose a simple technique to make these rigid prototypes more flexible by explicit modelling of invariances to translation, scaling and rotation. Results are reported on a task of handwritten Indian digits recognition.