Object recognition is a cornerstone task in autonomous and/or assistance systems like robots, autonomous vehicles, or those assisting to visually impaired, aiming to achieve a certain level of understanding of their surroundings. Probabilistic models, such as Conditional Random Fields (CRFs), have been successfully applied to this end given their ability to exploit contextual and situation information, e.g.a bowl is typically found in a cabinet and not in a night-stand. In this work we propose to evolve CRFs into Ontology-based Conditional Random Fields (obCRFs), which define a multi-level structure where each level assigns a category with different granularity to the same set of objects. For example, a level could assign to an object the category appliance or furniture, while the next one could categorize it into the tv, microwave, cabinet, or table types. In this way, general categorizations can guide the classification into more specialized ones (and vice versa), improving recognition success, and mitigating the CRFs limitations when modeling a high number of object categories (shared, in general, by Machine Learning techniques). To set the categories in each level we propose to mimic the hierarchical structure of ontologies, where categories are naturally codified following a subsumption ordering. This leads us to the second advantage of obCRFs: the multi-labeling of objects provides a richer understanding of the scene, which can be leveraged for accomplishing high-level tasks (e.g.object search or scheduling). Our approach has been tested with scenes from two state-of-the-art datasets: Robot@Home and Cornell-RGBD, outperforming the results provided by standard CRFs.
