TY - JOUR
T1 - Mapping Forests: A Comprehensive Approach for Nonlinear Mapping Problems
AU - Jampour, Mahdi
AU - Moin, Mohammad-Shahram
AU - Yu, Lap-Fai
AU - Bischof, Horst
PY - 2018
Y1 - 2018
N2 - A new and robust mapping approach is proposed entitled mapping forests (MFs) for computer vision applications based on regression transformations. Mapping forests relies on learning nonlinear mappings deduced from pairs of source and target training data, and improves the performance of mappings by enabling nonlinear transformations using forests. In contrast to previous approaches, it provides automatically selected mappings, which are naturally nonlinear. MF can provide accurate nonlinear transformations to compensate the gap of linear mappings or can generalize the nonlinear mappings with constraint kernels. In our experiments, we demonstrate that the proposed MF approach is not only on a par or better than linear mapping approaches and the state-of-the-art, but also is very time efficient, which makes it an attractive choice for real-time applications. We evaluated the efficiency and performance of the MF approach using the BU3DFE and Multi-PIE datasets for multi-view facial expression recognition application, and Set5, Set14 and SuperTex136 datasets for single image super-resolution application.
AB - A new and robust mapping approach is proposed entitled mapping forests (MFs) for computer vision applications based on regression transformations. Mapping forests relies on learning nonlinear mappings deduced from pairs of source and target training data, and improves the performance of mappings by enabling nonlinear transformations using forests. In contrast to previous approaches, it provides automatically selected mappings, which are naturally nonlinear. MF can provide accurate nonlinear transformations to compensate the gap of linear mappings or can generalize the nonlinear mappings with constraint kernels. In our experiments, we demonstrate that the proposed MF approach is not only on a par or better than linear mapping approaches and the state-of-the-art, but also is very time efficient, which makes it an attractive choice for real-time applications. We evaluated the efficiency and performance of the MF approach using the BU3DFE and Multi-PIE datasets for multi-view facial expression recognition application, and Set5, Set14 and SuperTex136 datasets for single image super-resolution application.
U2 - 10.1007/s10851-017-0755-z
DO - 10.1007/s10851-017-0755-z
M3 - Article
SN - 0924-9907
VL - 60
SP - 232
EP - 245
JO - Journal of Mathematical Imaging and Vision
JF - Journal of Mathematical Imaging and Vision
ER -