Image data mining and classification with DTree ensembles for linguistic tagging

Linguistic tagging of images require proper detection of language concepts from pictures, which is a challenging issue. Preparation of representative samples to demonstrate concepts is the first step; learning parameters from those training samples and setting up a classifier is the next step; proper tag set definition, extraction of relative contextual concepts, filtering and inference drawing for output tag set generation is the last step. A system based on a variant of an ensemble of decision trees classifier is developed which achieves promising results in classifying images targeted for linguistic tagging. A simple tag reconstruction strategy from classification voting information has been proposed.