TY - GEN
T1 - Labeling irregular graphs with belief propagation
AU - Nwogu, Ifeoma
AU - Corso, Jason J.
PY - 2008
Y1 - 2008
N2 - This paper proposes a statistical approach to labeling images using a more natural graphical structure than the pixel grid (or some uniform derivation of it such as square patches of pixels). Typically, low-level vision estimations based on graphical models work on the regular pixel lattice (with a known clique structure and neighborhood). We move away from this regular lattice to more meaningful statistics on which the graphical model, specifically the Markov network is defined. We create the irregular graph based on superpixels, which results in significantly fewer nodes and more natural neighborhood relationships between the nodes of the graph. Superpixels are a local, coherent grouping of pixels which preserves most of the structure necessary for segmentation. Their use reduces the complexity of the inferences made from the graphs with little or no loss of accuracy. Belief propagation (BP) is then used to efficiently find a local maximum of the posterior probability for this Markov network. We apply this statistical inference to finding (labeling) documents in a cluttered room (under moderately different lighting conditions).
AB - This paper proposes a statistical approach to labeling images using a more natural graphical structure than the pixel grid (or some uniform derivation of it such as square patches of pixels). Typically, low-level vision estimations based on graphical models work on the regular pixel lattice (with a known clique structure and neighborhood). We move away from this regular lattice to more meaningful statistics on which the graphical model, specifically the Markov network is defined. We create the irregular graph based on superpixels, which results in significantly fewer nodes and more natural neighborhood relationships between the nodes of the graph. Superpixels are a local, coherent grouping of pixels which preserves most of the structure necessary for segmentation. Their use reduces the complexity of the inferences made from the graphs with little or no loss of accuracy. Belief propagation (BP) is then used to efficiently find a local maximum of the posterior probability for this Markov network. We apply this statistical inference to finding (labeling) documents in a cluttered room (under moderately different lighting conditions).
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U2 - 10.1007/978-3-540-78275-9_26
DO - 10.1007/978-3-540-78275-9_26
M3 - Conference contribution
AN - SCOPUS:70349319547
SN - 3540782745
SN - 9783540782742
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 295
EP - 305
BT - Combinatorial Image Analysis - 12th International Workshop, IWCIA 2008, Proceedings
T2 - 12th International Workshop on Combinatorial Image Analysis, IWCIA 2008
Y2 - 7 April 2008 through 9 April 2008
ER -