TY - GEN
T1 - Infomax boosting
AU - Lyu, Siwei
PY - 2005
Y1 - 2005
N2 - In this paper, we described an efficient feature pursuit scheme for boosting. The proposed method is based on the infomax principle, which seeks optimal feature that achieves maximal mutual information with class labels. Direct feature pursuit with infomax is computationally prohibitive, so an efficient gradient ascent algorithm is further proposed, based on the quadratic mutual information, nonparametric density estimation and fast Gauss transform. The feature pursuit process is integrated into a boosting framework as infomax boosting. The performance of a face detector based on infomax boosting is reported.
AB - In this paper, we described an efficient feature pursuit scheme for boosting. The proposed method is based on the infomax principle, which seeks optimal feature that achieves maximal mutual information with class labels. Direct feature pursuit with infomax is computationally prohibitive, so an efficient gradient ascent algorithm is further proposed, based on the quadratic mutual information, nonparametric density estimation and fast Gauss transform. The feature pursuit process is integrated into a boosting framework as infomax boosting. The performance of a face detector based on infomax boosting is reported.
UR - https://www.scopus.com/pages/publications/24644516683
U2 - 10.1109/CVPR.2005.187
DO - 10.1109/CVPR.2005.187
M3 - Conference contribution
AN - SCOPUS:24644516683
SN - 0769523722
SN - 9780769523729
T3 - Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005
SP - 533
EP - 538
BT - Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005
PB - IEEE Computer Society
T2 - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005
Y2 - 20 June 2005 through 25 June 2005
ER -