PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Visual Classification of Images by Learning Geometric Appearances through Boosting
Martin Antenreiter, Christian Savu-Krohn and Peter Auer
In: ANNPR 2006, 31 Aug - 02 Sep 2006, Ulm, Germany.


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.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:1607
Deposited By:Christian Savu-Krohn
Deposited On:28 November 2005