In this paper, we propose a novel learning method, called Jensen-Shannon Boosting (JSBoost) and demonstrate its application to object recognition. JSBoost incorporates Jensen-Shannon (JS) divergence [Y. Bubner et al. (2001)] into AdaBoost learning. JS divergence is advantageous in that it provides more appropriate measure of dissimilarity between two classes and it is numerically more stable than other measures such as Kullback-Leibler (KL) divergence (see [Y. Bubner et al. (2001)]). The best features are iteratively learned by maximizing the projected JS divergence, based on which best weak classifiers are derived. The weak classifiers are combined into a strong one by minimizing the recognition error. JSBoost learning is demonstrated with face object recognition using a local binary pattern (LBP) [M. Pietikainen et al. (2004)] based representation. JSBoost selects the best LBP features from thousands of candidate features and constructs a strong classifier based on the selected features. JSBoost empirically produces better face recognition results than other AdaBoost variants such as RealBoost [R.E. Schapire et al. (1998)], GentleBoost [J. Friedman et al. (2000)] and KL-Boost [C. Liu et al. (2003)], as demonstrated by experiments.
Jensen-Shannon boosting learning for object recognition
2005-01-01
259667 byte
Conference paper
Electronic Resource
English
Graph Kernels from the Jensen-Shannon Divergence
British Library Online Contents | 2013
|An Analysis of Edge Detection by Using the Jensen-Shannon Divergence
British Library Online Contents | 2000
|Gaussian-weighted Jensen–Shannon divergence as a robust fitness function for multi-model fitting
British Library Online Contents | 2013
|Boosting Chain Learning for Object Detection
British Library Conference Proceedings | 2003
|TIBKAT | 1988
|