TY - GEN
T1 - InfoBoost for selecting discriminative Gabor features
AU - Bai, Li
AU - Shen, Linlin
PY - 2005
Y1 - 2005
N2 - We proposed a novel boosting algorithm - InfoBoost. Though AdaBoost has been widely used for feature selection and classifier learning, many of the selected features are redundant. By incorporating mutual information into AdaBoost, InfoBoost fully examines the redundancy between candidate classifiers and selected classifiers. The classifiers thus selected are both accurate and non-redundant. Experimental results show that InfoBoost learned strong classifier has lower training error than AdaBoost. InfoBoost learning has also been applied to selecting discriminative Gabor features for face recognition. Even with the simple correlation distance measure and 1-NN classifier, the selected Gabor features achieve quite high recognition accuracy on the FERET database, where both expression and illumination variance are present. When only 140 features are used, InfoBoost selected features achieve 95.5% accuracy, about 2.5% higher than that achieved by AdaBoost.
AB - We proposed a novel boosting algorithm - InfoBoost. Though AdaBoost has been widely used for feature selection and classifier learning, many of the selected features are redundant. By incorporating mutual information into AdaBoost, InfoBoost fully examines the redundancy between candidate classifiers and selected classifiers. The classifiers thus selected are both accurate and non-redundant. Experimental results show that InfoBoost learned strong classifier has lower training error than AdaBoost. InfoBoost learning has also been applied to selecting discriminative Gabor features for face recognition. Even with the simple correlation distance measure and 1-NN classifier, the selected Gabor features achieve quite high recognition accuracy on the FERET database, where both expression and illumination variance are present. When only 140 features are used, InfoBoost selected features achieve 95.5% accuracy, about 2.5% higher than that achieved by AdaBoost.
UR - http://www.scopus.com/inward/record.url?scp=33646125629&partnerID=8YFLogxK
U2 - 10.1007/11556121_52
DO - 10.1007/11556121_52
M3 - Conference contribution
AN - SCOPUS:33646125629
SN - 3540289690
SN - 9783540289692
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 423
EP - 432
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
T2 - 11th International Conference on Computer Analysis of Images and Patterns, CAIP 2005
Y2 - 5 September 2005 through 8 September 2005
ER -