Hierarchical Appearance-Based Classifiers for Qualitative Spatial Localization

This paper presents a novel appearance-based technique for qualitative spatial localization. A vocabulary of visual words is built automatically, representing local features that repeatedly occur in the set of training images. An information maximization technique is then applied to build a hierarchical classifier for each environment by learning informative visual words. Child nodes in this hierarchy encode information redundant with information coded by their parents. In localization, hierarchical classifiers are used in a top-down manner, where top-level visual words are examined first, and for each top-level visual word which does not respond as expected, its lower-level visual words are examined. This allows inference to recover from missing features encoded by higher-level visual words. Several experiments on a challenging localization database demonstrate the advantages of our hierarchical framework and show a significant improvement over the traditional bag-of-features approaches.