Transitioning from recognition to understanding in vision using additive Cartesian granule feature models

Abstract

Proposes an approach to object recognition that facilitates the transition from recognition to understanding. The proposed approach begins by segmenting the images into regions using standard image processing approaches, which are subsequently classified using a discovered fuzzy Cartesian granule feature classifier. Understanding is made possible through the transparent and succinct nature of the discovered models. The recognition of roads in images is taken as an illustrative problem in the vision domain. The discovered fuzzy models, while providing high levels of accuracy (97%), also provide understanding of the problem domain through the transparency of the learnt models. The learning step in the proposed approach is compared with other techniques, such as decision trees, naive Bayes methods and neural networks.