Scene object recognition is an essential requirement for intelligent mobile robots. In addition to geometric or appearance features, modern recogni- tion systems strive to incorporate contextual information, normally modelled through Probabilistic Graphical Models (PGMs) or Semantic Knowledge (SK). However, these approaches, separately, show some weaknesses that limit their application, e.g., the exponential complexity of the probabilistic inference over PGMs or the inability of SK to handle uncertainty. This pa- per presents a hybrid PGM-SK system for object recognition that integrates both techniques reducing their individual limitations and gaining in prob- abilistic inference efficiency, performance robustness, uncertainty handling, and providing coherent results according to domain knowledge codified by a human expert. We support this claim with an extensive experimental eval- uation according to both recognition success and time requirements in real scenarios from two datasets (NYU2 and UMA-offices). The yielded figures support the suitability of the hybrid PGM-SK recognition system, and its applicability to mobile robotic agents.
