Visual Classification of Images by Learning Geometric Appearances through Boosting
We present a multiclass classification system for gray value images through boosting. The feature selection is done using the LPBoost algorithm which selects suitable features of adequate type. In our experiments we use up to nine different kinds of feature types simultaneously. Furthermore, a greedy search strategy within the weak learner is used to find simple geometric relations between selected features from previous boosting rounds. The final hypothesis can also consist of more than one geometric model for an object class. Finally, we provide a weight optimization method for combining the learned one-vs-one classifiers for the multiclass classification. We tested our approach on a publicly available data set and compared our results to other state-of-the-art approaches, such as the ”bag of keypoints” method.