Representation and learning of invariance

Invariance is a very important property of features that are useful for vision. A great deal of research on this subject is going on at different labs. While invariance mechanisms can be prescribed for certain descriptors, it is our firm belief that this is not feasible for descriptors of higher level properties in general. As a consequence, these invariance mechanisms have to be learned by the vision system. In this paper, such a learning structure is proposed. A major contribution in this paper, as well as a crucial component for a successful operation, is the use of a coordinate-free information representation: the channel representation. Furthermore, each processing unit is a linear perceptron which operates on outer products of input data, implying a complex space of invariance. Two examples of how the representation can be employed are included. The examples shows an excellent separation of invariance modes, good accuracy, as well as a fast convergence