Probabilistic Graphical Models (PGMs) in general, and Undirected Graphical Models (UGMs) in particular, become suitable frameworks to capture and conveniently model the uncertainty inherent in a variety of problems. When applied to real world applications, such as scene object recognition, they turn into a reliable and widespread resorted tool. The effectiveness of UGMs is tight to the particularities of the problem to be solved and, especially, to the chosen learning strategy. This paper presents a review of practical, widely resorted learning approaches for Conditional Random Fields (CRFs), the discriminate variant of UGMs, which is completed with a thorough comparison and experimental analysis in the field of scene object recognition. The chosen application for UGMs is of particular interest given its potential for enhancing the capabilities of cognitive agents. Two state-of-the-art datasets, NYUv2 and Cornell-RGBD, containing intensity and depth imagery from indoor scenes are used for training and testing CRFs. Results regarding success rate, computational burden, and scalability are analyzed, including the benefits of using parallelization techniques for gaining in efficiency.
