Semantic object recognition by merging decision tree with object ontology

In this work, we propose an object recognition strategy in a domestic environment. Our contribution is to use low-level features extracted from images with high-level concepts generated from an ontology of domestic objects to get richer decision. It consists in developing a semantic classification by providing for a white cane user the class of the obstacle and the scene in which it is located. The classification is performed with a decision tree that provides a better recognition rate than SVM. The combination of color and texture features resolves the ambiguities of shape features for some objects that have similar shape.