RT Journal Article
T1 A survey on learning approaches for Undirected Graphical Models. Application to scene object recognition
A1 Ruiz-Sarmiento, José Raúl
A1 Galindo-Andrades, Cipriano
A1 González-Jiménez, Antonio Javier
K1 Reconocimiento de formas (Informática)
AB 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.
PB Elsevier
YR 2017
FD 2017
LK https://hdl.handle.net/10630/33862
UL https://hdl.handle.net/10630/33862
LA eng
NO Jose-Raul Ruiz-Sarmiento, Cipriano Galindo, Javier Gonzalez-Jimenez, A survey on learning approaches for Undirected Graphical Models. Application to scene object recognition, International Journal of Approximate Reasoning, Volume 83, 2017, Pages 434-451, ISSN 0888-613X, https://doi.org/10.1016/j.ijar.2016.10.009
NO This work is supported by the research projects TEP2012-530] and DPI2014-55826-R, funded by the An- dalusia Regional Government and the Spanish Government, respectively, both financed by European Regional Develop- ment’s funds (FEDER).
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 23 ene 2026