TY - GEN
T1 - On the effects of normalization in adaptive MRF hierarchies
AU - Chen, Albert Y.C.
AU - Corso, Jason J.
PY - 2010
Y1 - 2010
N2 - In this paper, we analyze the effects of energy normalization in adaptive-hierarchy-based energy minimization methods. Adaptive hierarchies provide a convenient multi-level abstraction of the underlying MRF. They have been shown to both accelerate computation and help avoid local minima. However, the standard recursive way of accumulating energy throughout the hierarchy causes energy terms to grow at different rates. Consequently, the faster-growing term, typically the unary term, dominates the overall energy at coarser level nodes, which hinders larger-scale energy/label change from happening. To solve the problem, we first investigate the theory and construction of adaptive hierarchies, then we analyze the theoretical bounds and expected values of its energy terms. Based on these analyses, we design and experimentally analyze three different energy-normalizing schemes. Our experiments show that properly normalized energies facilitate better use of the hierarchies during optimization: we observe an average improvement in the speed by 15% with the same accuracy.
AB - In this paper, we analyze the effects of energy normalization in adaptive-hierarchy-based energy minimization methods. Adaptive hierarchies provide a convenient multi-level abstraction of the underlying MRF. They have been shown to both accelerate computation and help avoid local minima. However, the standard recursive way of accumulating energy throughout the hierarchy causes energy terms to grow at different rates. Consequently, the faster-growing term, typically the unary term, dominates the overall energy at coarser level nodes, which hinders larger-scale energy/label change from happening. To solve the problem, we first investigate the theory and construction of adaptive hierarchies, then we analyze the theoretical bounds and expected values of its energy terms. Based on these analyses, we design and experimentally analyze three different energy-normalizing schemes. Our experiments show that properly normalized energies facilitate better use of the hierarchies during optimization: we observe an average improvement in the speed by 15% with the same accuracy.
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U2 - 10.1007/978-3-642-12712-0_25
DO - 10.1007/978-3-642-12712-0_25
M3 - Conference contribution
AN - SCOPUS:77952404338
SN - 3642127118
SN - 9783642127113
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 275
EP - 286
BT - Computational Modeling of Objects Represented in Images - Second International Symposium, CompIMAGE 2010, Proceedings
T2 - 2nd International Symposium on Computational Modeling of Objects Represented in Images, Fundamentals, Methods and Applications, CompIMAGE 2010
Y2 - 5 May 2010 through 7 May 2010
ER -