Segment and combine: a generic approach for supervised learning of invariant classifiers from topologically structured data

A generic method for supervised classification of structured objects is presented. The
approach induces a classifier by (i) deriving a surrogate dataset from a pre-classified
dataset of structured objects, by segmenting them into pieces, (ii) learning a model relating pieces to object-classes, (iii) classifying structured objects by combining predictions made for their pieces. The segmentation allows to exploit local information and can be adapted to inject invariances into the resulting classifier. The framework is illustrated on practical sequence, time-series and image classification problems.